CN117058063A - Battery defect detection method and device and electronic equipment - Google Patents
Battery defect detection method and device and electronic equipment Download PDFInfo
- Publication number
- CN117058063A CN117058063A CN202210462748.8A CN202210462748A CN117058063A CN 117058063 A CN117058063 A CN 117058063A CN 202210462748 A CN202210462748 A CN 202210462748A CN 117058063 A CN117058063 A CN 117058063A
- Authority
- CN
- China
- Prior art keywords
- image
- pairs
- battery
- defect detection
- characteristic point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000007547 defect Effects 0.000 title claims abstract description 217
- 238000001514 detection method Methods 0.000 title claims abstract description 103
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 36
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000012549 training Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 8
- 230000002950 deficient Effects 0.000 description 14
- 238000004519 manufacturing process Methods 0.000 description 14
- 230000009466 transformation Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 12
- 230000008569 process Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 239000000306 component Substances 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005336 cracking Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 230000008961 swelling Effects 0.000 description 2
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000036544 posture Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Quality & Reliability (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The application discloses a battery defect detection method and device and electronic equipment, and relates to the technical field of batteries. The method comprises the following steps: acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions; carrying out registration processing on the second image according to the first image; and determining a defect detection result of the battery according to the first image and the registered second image.
Description
Technical Field
The application belongs to the technical field of batteries, and particularly relates to a battery defect detection method, a device and electronic equipment.
Background
With the rapid development of battery technology, batteries are applied to various industries, particularly as core components of new energy automobiles. At present, not only is a requirement on a battery for large capacity, but also the quality of the battery is particularly concerned. However, since the production process of the battery is complicated, errors may occur in the production process, resulting in defects in the battery, for example, misalignment of components in the battery or external swelling, cracking, etc. of the battery may occur. At present, in the battery production process, although the battery is subjected to defect detection, the problem of low detection accuracy in the battery defect detection generally exists.
Disclosure of Invention
The embodiment of the application aims to provide a battery defect detection method, a device and electronic equipment, which can solve the problem that the detection accuracy is low in the existing battery defect detection.
In a first aspect, an embodiment of the present application provides a method for detecting a battery defect, including:
acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions;
carrying out registration processing on the second image according to the first image;
and determining a defect detection result of the battery according to the first image and the registered second image.
In the embodiment of the application, the first image and the second image of the battery with different dimensions are acquired, then the second image is registered according to the first image, and the battery defect detection result is determined according to the first image and the registered second image. Therefore, the method can realize the combination of registered image information of batteries with different dimensions and detect the defects of the batteries, thereby improving the accuracy of the determined defect detection result, namely improving the accuracy of the defect detection of the batteries.
In some embodiments, the first image and the second image each comprise N preset calibration objects, N being a positive integer,
The registering processing of the second image according to the first image includes:
according to the N calibration objects, N pairs of characteristic point pairs between the first image and the second image are determined, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises characteristic points of the corresponding preset calibration objects in the first image and the second image;
and carrying out registration processing on the second image according to the N pairs of characteristic points.
In this embodiment, N pairs of feature point pairs between the first image and the second image are determined through N preset calibration objects in the first image and the second image, so that efficiency of determining matched feature point pairs in the first image and the second image in the registration process can be improved, and efficiency of detecting defects of the battery is improved.
In some embodiments, the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
In the embodiment, the defect detection of the battery can be realized through the two-dimensional image and the three-dimensional image, so that the defect detection precision can be ensured, the calculation complexity can be reduced, and the defect detection efficiency can be improved.
In some embodiments, the determining N pairs of feature points between the first image and the second image according to the N calibration objects includes:
acquiring a gray level image of the two-dimensional image;
acquiring a brightness image and a depth image of the three-dimensional image;
according to the N calibration objects, N pairs of characteristic point pairs are determined in the gray level image and the brightness image;
the registering processing is performed on the second image according to the N pairs of feature points, including:
and carrying out registration processing on the depth image according to the N pairs of characteristic point pairs.
In this embodiment, the gray level image of the two-dimensional image, the luminance image and the depth image of the three-dimensional image are obtained, then N pairs of feature point pairs are determined in the gray level image and the luminance image according to N calibration objects, and finally the depth image is registered according to the N pairs of feature point pairs, so that the operand in the registration process can be reduced, and the registration efficiency is improved.
In some embodiments, the determining the defect detection result of the battery according to the first image and the registered second image includes:
determining at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
Determining an image area corresponding to each suspected defect area in the three-dimensional image according to the registered depth image;
determining a defect detection sub-result of each suspected defect area according to the depth of field information of the image area corresponding to each suspected defect area,
the defect detection result comprises a defect detection sub-result corresponding to the at least one suspected defect area.
In this embodiment, the defect detection of the battery is implemented by determining the suspected defect area in the grayscale image, and then determining the defect detection sub-result of each suspected defect area in the depth-of-field image based on the depth-of-field information of each suspected defect area. Thus, the calculation amount for identifying the defect area in the depth image can be reduced, and the defect detection efficiency of the battery can be improved.
In some embodiments, the determining N pairs of feature points between the first image and the second image according to the N calibration objects includes:
inputting the first image and the second image into a preset prediction model, outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model,
The preset prediction model is obtained through training at least one image sample, each image sample comprises a historical third image and a historical fourth image, and N pairs of historical characteristic point pairs between the third image and the fourth image; the N pairs of historical characteristic point pairs comprise characteristic points which are marked in the third image and the fourth image in advance based on the N preset calibration objects.
In this embodiment, the first image and the second image are input into the preset prediction model, and the N pairs of feature point pairs between the first image and the second image are output by the preset prediction model, so that the efficiency of determining the N pairs of feature point pairs between the first image and the second image can be improved, and the efficiency of detecting the defects of the battery can be further improved.
In some embodiments, before the capturing the first image and the second image obtained by shooting the battery, the method further includes:
calibrating an internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
In this embodiment, the accuracy of registration can be further improved by calibrating the reference matrix of the two-dimensional camera and shooting the first image based on the calibrated reference matrix, thereby improving the accuracy of detecting the defects of the battery.
In a second aspect, an embodiment of the present application provides a battery defect detection apparatus, including:
the image acquisition module is used for acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions;
the registration module is used for registering the second image according to the first image;
and the defect detection module is used for determining a defect detection result of the battery according to the first image and the registered second image.
In this embodiment, the first image and the second image of the battery with different dimensions are acquired, then the second image is registered according to the first image, and the battery defect detection result is determined according to the first image and the registered second image. Therefore, the method can realize the combination of registered image information of batteries with different dimensions and detect the defects of the batteries, thereby improving the accuracy of the determined defect detection result, namely improving the accuracy of the defect detection of the batteries.
In some embodiments, the first image and the second image each comprise N preset calibration objects, N being a positive integer,
the registration module includes:
The characteristic point pair determining unit is used for determining N pairs of characteristic point pairs between the first image and the second image according to the N calibration objects, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises characteristic points of the corresponding preset calibration objects in the first image and the second image;
and the registration unit is used for carrying out registration processing on the second image according to the N pairs of characteristic point pairs.
In this embodiment, N pairs of feature point pairs between the first image and the second image are determined through N preset calibration objects in the first image and the second image, so that efficiency of determining matched feature point pairs in the first image and the second image in the registration process can be improved, and efficiency of detecting defects of the battery is improved.
In some embodiments, the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
In the embodiment, the defect detection of the battery can be realized through the two-dimensional image and the three-dimensional image, so that the defect detection precision can be ensured, the calculation complexity can be reduced, and the defect detection efficiency can be improved.
In some embodiments, the feature point pair determining unit includes:
A two-dimensional image processing subunit, configured to acquire a gray-scale image of the two-dimensional image;
a three-dimensional image processing subunit, configured to acquire a luminance image and a depth image of the three-dimensional image;
the characteristic point pair determining subunit is used for determining N pairs of characteristic point pairs in the gray level image and the brightness image according to the N calibration objects;
the registration unit is specifically configured to:
and carrying out registration processing on the depth image according to the N pairs of characteristic point pairs.
In this embodiment, the gray level image of the two-dimensional image, the luminance image and the depth image of the three-dimensional image are obtained, then N pairs of feature point pairs are determined in the gray level image and the luminance image according to N calibration objects, and finally the depth image is registered according to the N pairs of feature point pairs, so that the operand in the registration process can be reduced, and the registration efficiency is improved.
In some embodiments, the defect detection module comprises:
a suspected defect area determining unit, configured to determine at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
an image area determining unit, configured to determine, in the three-dimensional image, an image area corresponding to each suspected defect area according to the registered depth image;
A defect sub-result determining unit, configured to determine a defect detection sub-result of each suspected defect area according to depth of field information of an image area corresponding to each suspected defect area,
the defect detection result comprises a defect detection sub-result corresponding to the at least one suspected defect area.
In this embodiment, the defect detection of the battery is implemented by determining the suspected defect area in the grayscale image, and then determining the defect detection sub-result of each suspected defect area in the depth-of-field image based on the depth-of-field information of each suspected defect area. Thus, the calculation amount for identifying the defect area in the depth image can be reduced, and the defect detection efficiency of the battery can be improved.
In some embodiments, the feature point pair determining unit is specifically configured to:
inputting the image information in the first image and the second image into a preset prediction model, outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model,
the preset prediction model is obtained through training at least one image sample, each image sample comprises a historical third image and a historical fourth image, and N pairs of historical characteristic point pairs between the third image and the fourth image; the N pairs of historical characteristic point pairs comprise characteristic points which are marked in the third image and the fourth image in advance based on the N preset calibration objects.
In this embodiment, the first image and the second image are input into the preset prediction model, and the N pairs of feature point pairs between the first image and the second image are output by the preset prediction model, so that the efficiency of determining the N pairs of feature point pairs between the first image and the second image can be improved, and the efficiency of detecting the defects of the battery can be further improved.
In some embodiments, further comprising:
a calibration module for calibrating the internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
In this embodiment, the accuracy of registration can be further improved by calibrating the reference matrix of the two-dimensional camera and shooting the first image based on the calibrated reference matrix, thereby improving the accuracy of detecting the defects of the battery.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor, the program or instruction implementing the steps of the method according to the first aspect when executed by the processor.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the method according to the first aspect.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic diagram of a battery defect detection system according to the present application;
FIG. 2 is a schematic flow chart of a method for detecting battery defects according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a device for detecting battery defects according to an embodiment of the present application; .
Fig. 4 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which are obtained by a person skilled in the art based on the embodiments of the present application, fall within the scope of protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
In the battery production process, due to the complex production process of the battery, errors may occur in the production link, and misalignment of components in the battery or external swelling, cracking, etc. of the battery may occur, thereby causing defects in the battery. Currently, in order to detect defects of a battery, a monocular camera is generally disposed on a production line of the battery, a two-dimensional image of the battery is photographed by the monocular camera, and a defective image area is identified from image information in the two-dimensional image. The image information in the two-dimensional image is limited, so that misjudgment can occur in the process of detecting the battery defect through the two-dimensional image, and the accuracy of detecting the battery defect is reduced.
In order to improve the accuracy of battery defect detection, the application provides a novel battery defect detection method, a device and electronic equipment.
Fig. 1 is a schematic diagram of a battery defect detection system according to an embodiment of the application. As shown in fig. 1, the battery defect detection system includes a first camera 11, a second camera 122, and an electronic device 13, where the first camera 11 and the second camera 12 are disposed on a production process line of the battery 20, and the first camera 11 and the second camera 12 respectively capture a first image and a second image of the battery 20 on the production process line, and transmit the first image and the second image to the electronic device 13, and the first camera 11 and the second camera 12 are cameras capturing images of different dimensions.
The first image and the second image may be images acquired at the same time or images acquired at different times, for example, the first camera 11 and the second camera 12 shown in fig. 1 acquire images of the same battery 20 at different times.
Fig. 2 is a flowchart of a battery defect detection method according to an embodiment of the application, which is applied to the electronic device 13. As shown in fig. 2, the method includes:
step 201, acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions;
step 202, registering the second image according to the first image;
step 203, determining a defect detection result of the battery according to the first image and the registered second image.
In the embodiment of the application, the first image and the second image of the battery with different dimensions are acquired, then the second image is registered according to the first image, and the battery defect detection result is determined according to the first image and the registered second image. Therefore, the method can realize the combination of registered image information of batteries with different dimensions and detect the defects of the batteries, thereby improving the accuracy of the determined defect detection result, namely improving the accuracy of the defect detection of the batteries.
In the above step 201, during the battery production process, the electronic device may acquire the first image and the second image obtained by photographing the battery.
The above-mentioned battery may refer to a single battery cell, may refer to a battery module composed of a plurality of battery cells, and the like.
For example, a two-dimensional camera (i.e., the first camera 11) and a four-dimensional camera (i.e., the second camera 12) may be disposed on a production process line of the battery cell, and the two-dimensional camera and the four-dimensional camera are used for photographing the appearance of the battery cell, respectively, so as to obtain a two-dimensional image (i.e., a first image) and a four-dimensional image (i.e., a second image).
The dimension of the first image may be larger than the dimension of the second image, or the dimension of the second image may be larger than the dimension of the first image, which is not limited herein.
In the step 202, after the electronic device acquires the first image and the second image, the electronic device may register the second image according to the first image.
The registering the second image according to the first image may be that feature extraction is performed on the first image and the second image, so as to obtain feature points in the first image and the second image respectively; matching the feature points in the first image and the second image to obtain matched feature point pairs; then obtaining image space coordinate transformation parameters through the matched characteristic point pairs; and finally, registering the images by the coordinate transformation parameters.
The feature extraction may be performed on the first image and the second image by a preset feature point detection algorithm, where feature points are detected in the first image and the second image. For example, feature points in the first image and the second image may be extracted by a scale-invariant feature transform (Scale Invariant Feature Transform, SIFT) algorithm or the like.
The matching of the feature points in the first image and the second image to obtain a matched feature point pair may be performed by a preset feature point matching algorithm to obtain the feature point pair in the first image and the second image. For example, it is possible to find feature points matching feature points in the first image among feature points of the second image by at least one of an optical flow method and a local descriptor-based method or the like.
The image space coordinate transformation parameters obtained by the matched feature point pairs may be obtained by performing space coordinate transformation processing on the feature points in the feature point pairs, and calculating the image space coordinate transformation parameters. Wherein the above-described spatial coordinate transformation processing may include at least one of point transformation, affine transformation (including at least one of scaling, rotation, translation, and the like), projective transformation, and the like.
In the step 203, after the electronic device performs the registration processing on the second image, the electronic device may determine a defect detection result of the battery according to the first image and the registered second image.
The determining the defect detection result of the battery according to the first image and the registered second image may be that the electronic device performs image recognition on the first image first to determine whether a suspected defect image area exists in the first image; if at least one suspected defect image area exists in the first image, the electronic equipment determines a corresponding image area in the registered second image according to the coordinates of each suspected defect image area; and finally, respectively carrying out defect detection on the image areas corresponding to the suspected defect image areas to obtain defect detection sub-results corresponding to the suspected defect image areas, so that whether the battery is defective or not can be determined through the defect detection sub-results corresponding to the suspected defect image areas.
The determination of whether or not the first image has the suspected defective image region may be an image region that determines whether or not the first image has at least one of dislocation, bulge, and crack.
The determining the defect detection result of the battery according to the first image and the registered second image may be identifying a first defect area satisfying a first preset defect condition in the first image, identifying a second defect area satisfying a second preset defect condition in the second image, and matching the identified first defect area and the second defect area to obtain a matched image area; and finally, determining a defect detection result of the battery according to the matched defect area.
In some embodiments, the first image and the second image each comprise N preset calibration objects, N being a positive integer.
The registering the second image according to the first image may include:
according to the N calibration objects, N pairs of characteristic point pairs between the first image and the second image are determined, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises the characteristic points of the corresponding preset calibration objects in the first image and the second image;
and carrying out registration processing on the second image according to the N pairs of characteristic points.
In this embodiment, N pairs of feature point pairs between the first image and the second image are determined through N preset calibration objects in the first image and the second image, so that efficiency of determining matched feature point pairs in the first image and the second image in the registration process can be improved, and efficiency of detecting defects of the battery is improved.
The N preset calibration objects may be preset reference calibration objects, and in the battery production process, the shapes and positions of the N preset calibration objects are fixed.
For example, in the case that the battery is a battery cell, in order to ensure the transmission of the battery cell on the production line, the battery cell is usually clamped by at least one clamping member and drives the battery cell to move, so that a calibration mark may be set on the at least one clamping cell to form at least one calibration mark (i.e., the N preset calibration objects), where the calibration mark may be a marked dot or the like.
The N preset calibration objects may be one calibration object or a plurality of calibration objects. Specifically, the N preset calibration objects may include 3 preset calibration objects, so that not only the accuracy of registration but also the registration efficiency may be ensured.
The determining N pairs of feature points between the first image and the second image according to the N calibration objects may include identifying, in the first image and the second image, an image area matching each calibration object as a feature point, and using the feature points identified in the first image and the second image as the feature point pairs corresponding to the calibration objects.
The matching processing is performed on the first image according to the N pairs of feature points, that is, the image space coordinate transformation parameters are obtained through the N pairs of feature points, and the image registration is performed by the coordinate transformation parameters.
In some embodiments, the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
In the embodiment, the defect detection of the battery can be realized through the two-dimensional image and the three-dimensional image, so that the defect detection precision can be ensured, the calculation complexity can be reduced, and the defect detection efficiency can be improved.
In the case where the first image includes a two-dimensional image and the second image includes a three-dimensional image, the registering processing of the second image according to the first image may be directly based on N calibration objects, and N pairs of feature points corresponding to the N calibration objects in the gray-scale image and the depth image of the two-dimensional image may be determined.
In some embodiments, determining N pairs of feature points between the first image and the second image according to the N calibration objects includes:
acquiring a gray level image of a two-dimensional image;
acquiring a brightness image and a depth image of a three-dimensional image;
And determining N pairs of characteristic point pairs in the gray level image and the brightness image according to the N calibration objects.
The registering the second image according to the N pairs of feature points may include:
and carrying out registration processing on the scene depth image according to the N pairs of characteristic point pairs.
In this embodiment, the gray level image of the two-dimensional image, the luminance image and the depth image of the three-dimensional image are obtained, then N pairs of feature point pairs are determined in the gray level image and the luminance image according to N calibration objects, and finally the depth image is registered according to the N pairs of feature point pairs, so that the operand in the registration process can be reduced, and the registration efficiency is improved.
Because the coordinates of each pixel point in the brightness image and the depth image of the three-dimensional image are consistent, the registration processing is performed on the depth image according to the N pairs of feature points, that is, the image space coordinate transformation parameters between the gray level image and the depth image can be obtained through the N pairs of feature points, and the image registration is performed on the depth image by the coordinate transformation parameters.
In the case of performing registration processing on the scene depth image according to the N pairs of feature point pairs, determining a defect detection result of the battery according to the first image and the registered second image may be respectively identifying a first defect area satisfying a first preset defect condition in the gray scale image, identifying a second defect area satisfying a second preset defect condition in the depth image, and matching the identified first defect area and the identified second defect area to obtain a matched image area; and finally, determining a defect detection result of the battery according to the matched defect area.
For example, at least one first defect region is identified in the gray scale image and at least one second defect region is identified in the depth image, and if the same defect region exists in the at least one first defect region and the at least one second defect region, the battery is determined to be defective and the same defect region is identified.
In some embodiments, determining a defect detection result of the battery from the first image and the registered second image includes:
determining at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
determining image areas corresponding to each suspected defect area in the three-dimensional image according to the registered depth image;
and determining a defect detection sub-result of each suspected defect area according to the depth of field information of the image area corresponding to each suspected defect area.
The defect detection result may include a defect detection sub-result corresponding to at least one suspected defect area.
In this embodiment, the defect detection of the battery is implemented by determining the suspected defect area in the grayscale image, and then determining the defect detection sub-result of each suspected defect area in the depth-of-field image based on the depth-of-field information of each suspected defect area. Thus, the calculation amount for identifying the defect area in the depth image can be reduced, and the defect detection efficiency of the battery can be improved.
Because the coordinates of each pixel point in the depth image and the pixels in the gray level image after registration have a mapping relationship, after the electronic device determines the coordinates of each suspected defect image area, the electronic device can acquire the coordinates of the pixel points in each suspected defect image area, determine the pixels in the depth image, which have a mapping relationship with the coordinates of the pixel points in each suspected defect image area, according to the coordinates of the pixel points in each suspected defect image area, and determine the image area including the determined pixels as the image area corresponding to the suspected defect image area.
The performing defect detection on the image area corresponding to each suspected defect image area to obtain a defect detection sub-result corresponding to each suspected defect image area may be determining whether the depth of field information meets a preset condition according to the depth of field information of the image area corresponding to each suspected defect image area, if the depth of field information meets the preset condition, the obtained defect detection sub-result indicates that the suspected defect image area is the image area with the defect; if the preset condition is not met, the obtained defect detection sub-result indicates that the suspected defect image area is an image area without defects.
For example, it may be determined whether an average depth of field value in the depth of field information of the image area corresponding to each suspected defective image area is greater than or equal to a preset depth of field value, if so, the suspected defective image area is determined to be the image area with the defect, that is, the defect detection sub-result indicates that the suspected defective image area is the image area with the defect; otherwise, determining the suspected defect image area as an image area without defects, namely, the defect detection sub-result indicates that the suspected defect image area is an image area without defects.
The determination of whether the battery is defective or not according to the defect detection sub-result corresponding to each suspected defective image area may be performed by determining whether the battery is defective or not according to the area, the number, and the like of the image areas in the at least one suspected defective image area defect detection sub-result.
For example, in the defect detection sub-result of the at least one suspected defective image area defect, if the number of image areas indicating that the corresponding suspected defective image area defect is a defect is greater than a preset number, it may be determined whether the battery is defective.
In some embodiments, determining N pairs of feature points between the first image and the second image from the N calibration objects includes:
and inputting the first image and the second image into a preset prediction model, and outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model.
The method comprises the steps that a preset prediction model is obtained through training of at least one image sample, each image sample comprises a third image and a fourth image of a history, and N pairs of history feature point pairs between the third image and the fourth image; the N pairs of history feature point pairs comprise feature points which are marked in the third image and the fourth image in advance based on N preset calibration objects.
In this embodiment, the first image and the second image are input into the preset prediction model, and the N pairs of feature point pairs between the first image and the second image are output by the preset prediction model, so that the efficiency of determining the N pairs of feature point pairs between the first image and the second image can be improved, and the efficiency of detecting the defects of the battery can be further improved.
The preset prediction model may be obtained by training the at least one image training sample in advance, that is, before the first image and the second image are input into the preset prediction model, the method further includes: and inputting the at least one image training sample into the deep neural model, and iteratively updating network parameters of the deep neural model through the at least one image training sample to obtain the preset prediction model. Since the process of training the deep nerve model by training samples is known, a detailed description thereof will not be given here.
The third image may be an image corresponding to the first image, that is, the third image and the first image are images having the same dimensional image data; similarly, the fourth image is an image corresponding to the second image.
The N pairs of history feature point pairs may be feature point pairs corresponding to N preset calibration objects, which are labeled in advance in the third image and the fourth image by a human.
In some embodiments, before acquiring the first image and the second image obtained by photographing the battery, the method further includes:
calibrating an internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
In this embodiment, the accuracy of registration can be further improved by calibrating the reference matrix of the two-dimensional camera and shooting the first image based on the calibrated reference matrix, thereby improving the accuracy of detecting the defects of the battery.
The calibration of the internal reference matrix of the two-dimensional camera can be performed by acquiring calibration plate images under different postures based on a Zhang Zhengyou calibration method.
The calibration of the reference matrix of the two-dimensional camera can be performed under the condition of starting the production line of the battery each time; alternatively, the calibration may be performed in a case where a shooting scene of the two-dimensional camera is changed, for example, in a case where the two-dimensional camera is moved or the like, so as to calibrate an internal reference matrix of the two-dimensional camera.
Fig. 3 is a schematic structural diagram of a battery defect detecting device according to an embodiment of the application. As shown in fig. 3, the apparatus 300 includes:
an image acquisition module 301, configured to acquire a first image and a second image obtained by shooting a battery, where the first image and the second image include images with different dimensions;
a registration module 302, configured to perform registration processing on the second image according to the first image;
the defect detection module 303 is configured to determine a defect detection result of the battery according to the first image and the registered second image.
In some embodiments, the first image and the second image each comprise N preset calibration objects, N being a positive integer.
Registration module 302 may include:
the characteristic point pair determining unit is used for determining N pairs of characteristic point pairs between the first image and the second image according to the N calibration objects, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises the characteristic points of the corresponding preset calibration objects in the first image and the second image;
and the registration unit is used for registering the second image according to the N pairs of characteristic point pairs.
In some embodiments, the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
In some embodiments, the feature point pair determining unit includes:
a two-dimensional image processing subunit, configured to acquire a gray-scale image of the two-dimensional image;
the three-dimensional image processing subunit is used for acquiring a brightness image and a depth image of the three-dimensional image;
and the characteristic point pair determining subunit is used for determining N pairs of characteristic point pairs in the gray level image and the brightness image according to the N calibration objects.
A registration unit, in particular for:
and carrying out registration processing on the scene depth image according to the N pairs of characteristic point pairs.
In some embodiments, defect detection module 303 includes:
the suspected defect area determining unit is used for determining at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
an image area determining unit, configured to determine an image area corresponding to each suspected defect area in the three-dimensional image according to the registered depth image;
and the defect sub-result determining unit is used for determining a defect detection sub-result of each suspected defect area according to the depth information of the image area corresponding to each suspected defect area.
The defect detection result may include a defect detection sub-result corresponding to at least one suspected defect area.
In some embodiments, the feature point pair determining unit is specifically configured to:
And inputting the image information in the first image and the second image into a preset prediction model, and outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model.
The preset prediction model can be obtained through training at least one image sample, each image sample comprises a third image and a fourth image of a history, and N pairs of history feature point pairs between the third image and the fourth image; the N pairs of history feature point pairs comprise feature points which are pre-marked in the third image and the fourth image based on N preset calibration objects.
In some embodiments, further comprising:
a calibration module for calibrating the internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
Other details of the battery defect detection device according to the embodiment of the present application are similar to those of the battery defect detection method described above in connection with the example shown in fig. 2, and can achieve the corresponding technical effects, and for brevity, the description is omitted here.
Fig. 4 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.
The electronic device may comprise a processor 401 and a memory 402 in which computer program instructions are stored.
In particular, the processor 401 described above may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing embodiments of the present application.
Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. In some examples, memory 402 may include removable or non-removable (or fixed) media, or memory 402 may be a non-volatile solid state memory. In some embodiments, the memory 402 may be internal or external to the battery device.
In some examples, memory 402 may be Read Only Memory (ROM). In one example, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
Memory 402 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement the method in the embodiment shown in fig. 2, and achieves the corresponding technical effects achieved by executing the method/steps in the embodiment shown in fig. 2, which are not described herein for brevity.
In one example, the electronic device may also include a communication interface 403 and a bus 404. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected to each other by a bus 404 and perform communication with each other.
The communication interface 403 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.
Bus 404 includes hardware, software, or both, coupling the components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, an infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (MCa) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a video electronics standards association local (VLB) Bus, or other suitable Bus, or a combination of two or more of the above. Bus 404 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The electronic device may perform the battery defect detection method in the embodiment of the present application, thereby implementing the battery defect detection method and apparatus described in connection with fig. 2 and 3.
In addition, in combination with the method and the device for detecting the battery defect in the above embodiments, the embodiments of the present application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any of the batteries of the above embodiments and a control method thereof.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices, and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.
Claims (16)
1. A battery defect detection method, characterized by comprising:
acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions;
carrying out registration processing on the second image according to the first image;
And determining a defect detection result of the battery according to the first image and the registered second image.
2. The method of claim 1, wherein the first image and the second image each comprise N preset calibration objects, N being a positive integer,
the registering processing of the second image according to the first image includes:
according to the N calibration objects, N pairs of characteristic point pairs between the first image and the second image are determined, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises characteristic points of the corresponding preset calibration objects in the first image and the second image;
and carrying out registration processing on the second image according to the N pairs of characteristic points.
3. The method of claim 2, wherein the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
4. A method according to claim 3, wherein said determining N pairs of feature points between the first image and the second image from the N calibration objects comprises:
acquiring a gray level image of the two-dimensional image;
Acquiring a brightness image and a depth image of the three-dimensional image;
according to the N calibration objects, N pairs of characteristic point pairs are determined in the gray level image and the brightness image;
the registering processing is performed on the second image according to the N pairs of feature points, including:
and carrying out registration processing on the depth image according to the N pairs of characteristic point pairs.
5. The method of claim 4, wherein determining a defect detection result of the battery from the first image and the registered second image comprises:
determining at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
determining an image area corresponding to each suspected defect area in the three-dimensional image according to the registered depth image;
determining a defect detection sub-result of each suspected defect area according to the depth of field information of the image area corresponding to each suspected defect area,
the defect detection result comprises a defect detection sub-result corresponding to the at least one suspected defect area.
6. The method of claim 2, wherein the determining N pairs of feature points between the first image and the second image from the N calibration objects comprises:
Inputting the first image and the second image into a preset prediction model, outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model,
the preset prediction model is obtained through training at least one image sample, each image sample comprises a historical third image and a historical fourth image, and N pairs of historical characteristic point pairs between the third image and the fourth image; the N pairs of historical characteristic point pairs comprise characteristic points which are marked in the third image and the fourth image in advance based on the N preset calibration objects.
7. The method of claim 1, further comprising, prior to acquiring the first image and the second image of the battery:
calibrating an internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
8. A battery defect detection apparatus, characterized by comprising:
the image acquisition module is used for acquiring a first image and a second image obtained by shooting a battery, wherein the first image and the second image comprise images with different dimensions;
The registration module is used for registering the second image according to the first image;
and the defect detection module is used for determining a defect detection result of the battery according to the first image and the registered second image.
9. The apparatus of claim 8, wherein the first image and the second image each comprise N preset calibration objects, N being a positive integer,
the registration module includes:
the characteristic point pair determining unit is used for determining N pairs of characteristic point pairs between the first image and the second image according to the N calibration objects, the N pairs of characteristic point pairs are in one-to-one correspondence with the N preset calibration objects, and each characteristic point pair comprises characteristic points of the corresponding preset calibration objects in the first image and the second image;
and the registration unit is used for carrying out registration processing on the second image according to the N pairs of characteristic point pairs.
10. The apparatus of claim 9, wherein the first image comprises a two-dimensional image and the second image comprises a three-dimensional image.
11. The apparatus according to claim 10, wherein the feature point pair determining unit includes:
A two-dimensional image processing subunit, configured to acquire a gray-scale image of the two-dimensional image;
a three-dimensional image processing subunit, configured to acquire a luminance image and a depth image of the three-dimensional image;
the characteristic point pair determining subunit is used for determining N pairs of characteristic point pairs in the gray level image and the brightness image according to the N calibration objects;
the registration unit is specifically configured to:
and carrying out registration processing on the depth image according to the N pairs of characteristic point pairs.
12. The apparatus of claim 11, wherein the defect detection module comprises:
a suspected defect area determining unit, configured to determine at least one suspected defect area of the battery in the two-dimensional image according to the gray level image;
an image area determining unit, configured to determine, in the three-dimensional image, an image area corresponding to each suspected defect area according to the registered depth image;
a defect sub-result determining unit, configured to determine a defect detection sub-result of each suspected defect area according to depth of field information of an image area corresponding to each suspected defect area,
the defect detection result comprises a defect detection sub-result corresponding to the at least one suspected defect area.
13. The apparatus according to claim 9, wherein the feature point pair determining unit is specifically configured to:
inputting the image information in the first image and the second image into a preset prediction model, outputting N pairs of characteristic point pairs between the first image and the second image through the preset prediction model,
the preset prediction model is obtained through training at least one image sample, each image sample comprises a historical third image and a historical fourth image, and N pairs of historical characteristic point pairs between the third image and the fourth image; the N pairs of historical characteristic point pairs comprise characteristic points which are marked in the third image and the fourth image in advance based on the N preset calibration objects.
14. The apparatus as recited in claim 8, further comprising:
a calibration module for calibrating the internal reference matrix of the two-dimensional camera,
the first image is an image obtained by shooting the two-dimensional camera based on the calibrated internal reference matrix.
15. An electronic device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which program or instruction when executed by the processor implements the steps of the method of any of claims 1-7.
16. A readable storage medium, characterized in that it stores thereon a program or instructions, which when executed by a processor, implement the steps of the method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210462748.8A CN117058063A (en) | 2022-04-28 | 2022-04-28 | Battery defect detection method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210462748.8A CN117058063A (en) | 2022-04-28 | 2022-04-28 | Battery defect detection method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117058063A true CN117058063A (en) | 2023-11-14 |
Family
ID=88661324
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210462748.8A Pending CN117058063A (en) | 2022-04-28 | 2022-04-28 | Battery defect detection method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117058063A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117606370A (en) * | 2023-12-25 | 2024-02-27 | 苏州富鑫林光电科技有限公司 | Chip pin detection method and device for semiconductor disc products |
| CN117723551A (en) * | 2024-02-18 | 2024-03-19 | 宁德时代新能源科技股份有限公司 | Battery detection device, point detection method, battery production device and detection method |
-
2022
- 2022-04-28 CN CN202210462748.8A patent/CN117058063A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117606370A (en) * | 2023-12-25 | 2024-02-27 | 苏州富鑫林光电科技有限公司 | Chip pin detection method and device for semiconductor disc products |
| CN117723551A (en) * | 2024-02-18 | 2024-03-19 | 宁德时代新能源科技股份有限公司 | Battery detection device, point detection method, battery production device and detection method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108124487B (en) | Cloud meter reading method and device | |
| CN107492091B (en) | Label appearance detection method based on machine vision and terminal equipment | |
| CN108229475B (en) | Vehicle tracking method, system, computer device and readable storage medium | |
| CN111681256B (en) | Image edge detection method, image edge detection device, computer equipment and readable storage medium | |
| CN110493595B (en) | Camera detection method and device, storage medium and electronic device | |
| CN117058063A (en) | Battery defect detection method and device and electronic equipment | |
| CN111667448A (en) | Image processing method, device and equipment | |
| CN104363815A (en) | Image processing device, image processing method, and image processing program | |
| CN110084830B (en) | Video moving object detection and tracking method | |
| CN117635590B (en) | Defect detection method and device for notebook computer shell | |
| CN117677969A (en) | Defect detection method and device | |
| CN110866949A (en) | Center point positioning method and device, electronic equipment and storage medium | |
| CN117094975A (en) | Method and device for detecting surface defects of steel and electronic equipment | |
| CN115512381A (en) | Text recognition method, text recognition device, text recognition equipment, storage medium and working machine | |
| CN111311671A (en) | Workpiece measuring method and device, electronic equipment and storage medium | |
| CN117571721B (en) | Method and device for detecting surface defects of circuit board bonding pad and storage medium | |
| CN111336938A (en) | Robot and object distance detection method and device thereof | |
| CN116993654B (en) | Camera module defect detection method, device, equipment, storage medium and product | |
| CN116664817A (en) | Power device state change detection method based on image difference | |
| CN117146739A (en) | Angle measurement verification method and system for optical sighting telescope | |
| CN117635599A (en) | Defect detection model training and defect detection method, device, equipment and medium | |
| CN116434556A (en) | Vehicle information acquisition method, device, equipment and medium thereof | |
| CN116503413A (en) | Filament quality intelligent detection method based on computer vision | |
| CN118660145B (en) | Security check image conversion method, system, equipment and storage medium | |
| CN116934847A (en) | Discharging method, discharging device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |