CN111091535A - Factory management method and system based on deep learning image semantic segmentation - Google Patents
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Abstract
The invention provides a factory management method and a system based on deep learning image semantic segmentation, which are applied to a factory management system and comprise the following steps: performing semantic segmentation on the image based on a full convolution network of deep learning to obtain the category of each pixel, wherein the image is an image in a factory acquired through a factory management system; extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel; performing shape analysis on the target area to obtain an analysis result, wherein the analysis result comprises any one of the following items: meeting the regulation, not meeting the regulation; and if the analysis result is that the rule is not met, sending out an early warning signal. The invention solves the technical problems of high difficulty and low detection accuracy of target detection with large shape change in the prior art.
Description
Technical Field
The invention relates to the technical field of factory management, in particular to a factory management method and system based on deep learning image semantic segmentation.
Background
The 6S management (i.e. consolidation (SEIRI), consolidation (SEITON), cleaning (SEISO), cleaning (SEIKETSU), literacy (SHITSUKE), SECURITY (SECURITY), 6S management for short) is an important method for modern plant management, including various links such as human, machine, material, law, and ring. In the prior art, a method for performing real-time online analysis on each dimension information managed by 6S in a factory building mainly detects pedestrians and materials by a method based on rectangular frame detection. However, the object without fixed shape, such as stain and material, is difficult to be detected and solved by the rectangular frame with fixed shape and by the uniform detector. Therefore, the analysis method for directly performing instance segmentation in the on-line management of the factory 6S in the prior art has the technical problems of high difficulty in detecting the target with large shape change and low detection accuracy.
Disclosure of Invention
In view of the above, the present invention provides a factory management method and system based on deep learning image semantic segmentation, so as to alleviate technical problems in the prior art that the detection of a target with a large shape change is difficult and the detection accuracy is not high.
In a first aspect, an embodiment of the present invention provides a factory management method based on deep learning image semantic segmentation, which is applied to a factory management system, and includes: performing semantic segmentation on an image based on a full convolution network of deep learning to obtain the category of each pixel, wherein the image is an internal image of a factory, which is obtained through the factory management system; extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel; performing shape analysis on the target region to obtain an analysis result, wherein the analysis result comprises any one of the following items: meeting the regulation, not meeting the regulation; and if the analysis result is not in accordance with the regulation, sending out an early warning signal.
Further, the target region includes any one of: a stain area, a channel area, a material area, a worker area; performing shape analysis on the stained area to obtain an analysis result, wherein the analysis result comprises the following steps: and analyzing the shape of the dirty area, and if the area of the dirty area is larger than a preset area, obtaining an analysis result which does not accord with the regulation.
Further, performing shape analysis on the channel region or the material region to obtain an analysis result, including: and analyzing the channel area or the material area, and if the placement of the materials is found in the channel area or a preset area in the material area, obtaining an analysis result which does not accord with the regulation.
Further, performing shape analysis on the worker area to obtain an analysis result, including: and extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result which does not accord with the regulation if the workers are judged not to wear safety helmets.
In a second aspect, an embodiment of the present invention further provides a factory management system based on deep learning image semantic segmentation, including: the system comprises a semantic segmentation module, an area extraction module, an analysis module and an early warning module, wherein the semantic segmentation module is used for performing semantic segmentation on an image based on a full convolution network of deep learning to obtain the category of each pixel, and the image is an image in a factory acquired through the factory management system; the region extraction module is used for extracting a target region of the image based on the category and the shape of the circumscribed convex hull of each pixel; the analysis module is configured to perform shape analysis on the target region to obtain an analysis result, where the analysis result includes any one of: meeting the regulation, not meeting the regulation; and the early warning module is used for sending out an early warning signal if the analysis result does not accord with the regulation.
Further, the target region includes any one of: a stain area, a channel area, a material area, a worker area; the analysis module further comprises a first analysis unit for: and analyzing the shape of the dirty area, and if the area of the dirty area is larger than a preset area, obtaining an analysis result which does not accord with the regulation.
Further, the analysis module further comprises a second analysis unit for: and analyzing the channel area or the material area, and if the placement of the materials is found in the channel area or a preset area in the material area, obtaining an analysis result which does not accord with the regulation.
Further, the analysis module further comprises a third analysis unit for: and extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result which does not accord with the regulation if the workers are judged not to wear safety helmets.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a computer-readable medium having non-volatile program code executable by a processor, where the program code causes the processor to execute the method according to the first aspect.
The invention provides a factory management method and a system based on deep learning image semantic segmentation, which are applied to a factory management system and comprise the following steps: performing semantic segmentation on the image based on a deep learning full convolution network to obtain the category of each pixel; extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel; carrying out shape analysis on the target area to obtain an analysis result; and if the analysis result is that the rule is not met, sending out an early warning signal. The method is based on a semantic segmentation method and combines extraction of convex hulls to uniformly detect various targets, and the problems that a detection model has large morphological change and poor fluid effect are solved, so that the technical problems that the detection of the targets with large shape change is difficult and the detection accuracy is low in the prior art are solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a factory management method based on deep learning image semantic segmentation according to an embodiment of the present invention;
FIG. 2 is a flowchart of another factory management method based on deep learning image semantic segmentation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a factory management system based on deep learning image semantic segmentation according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another factory management system based on deep learning image semantic segmentation according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in the prior art, a method for performing real-time online analysis on each dimension information managed by 6S in a factory building mainly detects pedestrians and materials by a method based on rectangular frame detection. However, the object without fixed shape, such as stain and material, is difficult to be detected and solved by the rectangular frame with fixed shape and by the uniform detector. Therefore, the invention provides a factory management method based on deep learning image semantic segmentation to classify various targets at semantic pixel level, and then extract each relevant area in a convex packet extraction mode to solve the detection problem of various interested targets.
Fig. 1 is a flowchart of a factory management method based on deep learning image semantic segmentation, which is applied to a factory management system according to an embodiment of the present invention. As shown in fig. 1, the method specifically includes the following steps:
step S102, performing semantic segmentation on the image based on the full convolution network of the deep learning to obtain the category of each pixel, wherein the image is an internal image of the factory obtained through a factory management system, and the category comprises any one of the following items: workers, materials, floors, stains.
Specifically, a real-time monitoring image in a factory is obtained through a monitoring device in the factory, and then semantic segmentation operation is performed on the image based on a deep learning full convolution network to obtain the category of each pixel. Alternatively, the image may be subjected to semantic segmentation operation by using FCN (full volume Networks), or deplab algorithm.
Step S104, extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel, wherein the target area comprises any one of the following items: stain area, channeling area, material area, worker area.
Step S106, carrying out shape analysis on the target area to obtain an analysis result, wherein the analysis result comprises any one of the following items: compliance with the regulations, non-compliance with the regulations.
And step S108, if the analysis result is that the rule is not met, sending out an early warning signal.
The invention provides a factory management method based on deep learning image semantic segmentation, which is applied to a factory management system and comprises the following steps: performing semantic segmentation on the image based on a deep learning full convolution network to obtain the category of each pixel; extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel; carrying out shape analysis on the target area to obtain an analysis result; and if the analysis result is that the rule is not met, sending out an early warning signal. The method is based on a semantic segmentation method and combines extraction of convex hulls to uniformly detect various targets, and the problems that a detection model has large morphological change and poor fluid effect are solved, so that the technical problems that the detection of the targets with large shape change is difficult and the detection accuracy is low in the prior art are solved.
Specifically, in step S106, the step of performing shape analysis on the stained area to obtain an analysis result includes:
and analyzing the shape of the dirty area, and if the area of the dirty area is larger than the preset area, obtaining an analysis result which does not accord with the regulation. Namely, when the fact that the area of the stain in the factory is larger than the preset area is detected, the fact that the stain needing to be cleaned exists in the factory is indicated, an early warning signal can be sent out, and workers are reminded to clean the stain in the target area.
Specifically, in step S106, the step of performing shape analysis on the channel region or the material region to obtain an analysis result includes:
and analyzing the channel area or the material area, and if the placement of the materials is found in a preset area in the channel area or the material area, obtaining an analysis result which does not accord with the regulation. Optionally, whether the materials are randomly arranged or not is judged based on the channel area and the material area, and if yes, an early warning signal is sent out.
Specifically, in step S106, the step of performing shape analysis on the worker area to obtain an analysis result includes:
and extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result which does not accord with the regulation if the workers are judged not to wear the safety helmet. For example, workers in a worker area are extracted, the head state of the workers is analyzed, and if the workers are judged not to wear safety helmets, an early warning signal is sent out.
Optionally, fig. 2 is a flowchart of another factory management method based on deep learning image semantic segmentation according to an embodiment of the present invention. As shown in fig. 2, the method includes:
inputting a video real-time stream;
performing semantic segmentation processing on the image of the video real-time stream;
respectively extracting a stain area, a channel area, a material area and a worker area;
and finally, early warning of stain events, early warning of disordered placement of materials or early warning of safety helmets of workers is carried out according to the extraction result.
As can be seen from the above description, the factory management method based on deep learning image semantic segmentation provided by the embodiment of the present invention uniformly solves the detection problem of different targets, improves the problem that different targets adopt different detection algorithms, alleviates the technical problems of large detection difficulty and low detection accuracy of the targets with large shape changes in the prior art, reduces the workload of data acquisition and labeling, solves a plurality of problems in an end-to-end manner through one method, and achieves the technical effects of strong expandability and low maintenance cost.
Example two:
FIG. 3 is a schematic diagram of a factory management system based on deep learning image semantic segmentation according to an embodiment of the present invention, as shown in FIG. 3, the system includes: the system comprises a semantic segmentation module 10, a region extraction module 20, an analysis module 30 and an early warning module 40.
Specifically, the semantic segmentation module 10 is configured to perform semantic segmentation on an image based on a deep learning full convolution network to obtain a category of each pixel, where the image is an internal image of a plant acquired through a plant management system, and the category includes any one of the following items: workers, materials, floors, stains.
A region extraction module 20, configured to perform target region extraction on the image based on the category and the shape of the circumscribed convex hull of each pixel, where the target region includes any one of: stain area, channeling area, material area, worker area.
The analysis module 30 is configured to perform shape analysis on the target region to obtain an analysis result, where the analysis result includes any one of: compliance with the regulations, non-compliance with the regulations.
And the early warning module 40 is used for sending out an early warning signal if the analysis result is that the analysis result does not meet the regulation.
According to the factory management system based on deep learning image semantic segmentation provided by the embodiment of the invention, the semantic segmentation is carried out on an image through a semantic segmentation module based on a full convolution network of deep learning to obtain the category of each pixel; extracting a target region of the image based on the category and the shape of the circumscribed convex hull of each pixel through a region extraction module; carrying out shape analysis on the target area through an analysis module to obtain an analysis result; and finally, if the analysis result is that the analysis result does not accord with the regulation, an early warning signal is sent out through an early warning module. The method is based on a semantic segmentation method and combines extraction of convex hulls to uniformly detect various targets, and the problems that a detection model has large morphological change and poor fluid effect are solved, so that the technical problems that the detection of the targets with large shape change is difficult and the detection accuracy is low in the prior art are solved.
Optionally, fig. 4 is a schematic diagram of another factory management system based on deep learning image semantic segmentation according to an embodiment of the present invention, and as shown in fig. 4, the analysis module 30 in the system further includes a first analysis unit 31, a second analysis unit 32, and a third analysis unit 33.
Specifically, the first analysis unit 31 is configured to perform shape analysis on the dirty region, and if the area of the dirty region is larger than a preset area, obtain an analysis result that does not meet the specification.
And the second analysis unit 32 is used for analyzing the channel area or the material area, and if the placement of the materials is found in a preset area in the channel area or the material area, an analysis result which does not meet the regulation is obtained.
And a third analyzing unit 33 for extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result that does not meet the specification if it is determined that the workers do not wear the safety helmet.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method in the foregoing embodiment are implemented.
Embodiments of the present invention also provide a computer readable medium having non-volatile program code executable by a processor, where the program code causes the processor to perform the steps of the method of one of the above embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A factory management method based on deep learning image semantic segmentation is characterized by being applied to a factory management system and comprising the following steps:
performing semantic segmentation on an image based on a full convolution network of deep learning to obtain the category of each pixel, wherein the image is an internal image of a factory, which is obtained through the factory management system;
extracting a target area of the image based on the category and the shape of the circumscribed convex hull of each pixel;
performing shape analysis on the target region to obtain an analysis result, wherein the analysis result comprises any one of the following items: meeting the regulation, not meeting the regulation;
and if the analysis result is not in accordance with the regulation, sending out an early warning signal.
2. The method of claim 1, wherein the target region comprises any one of: a stain area, a channel area, a material area, a worker area; performing shape analysis on the stained area to obtain an analysis result, wherein the analysis result comprises the following steps:
and analyzing the shape of the dirty area, and if the area of the dirty area is larger than a preset area, obtaining an analysis result which does not accord with the regulation.
3. The method of claim 2, wherein analyzing the shape of the channel region or the material region to obtain an analysis result comprises:
and analyzing the channel area or the material area, and if the placement of the materials is found in the channel area or a preset area in the material area, obtaining an analysis result which does not accord with the regulation.
4. The method of claim 2, wherein performing a shape analysis of the worker area to obtain an analysis result comprises:
and extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result which does not accord with the regulation if the workers are judged not to wear safety helmets.
5. A factory management system based on deep learning image semantic segmentation is characterized by comprising: a semantic segmentation module, a region extraction module, an analysis module and an early warning module, wherein,
the semantic segmentation module is used for performing semantic segmentation on the image based on a full convolution network of deep learning to obtain the category of each pixel, wherein the image is an image in the factory acquired through the factory management system;
the region extraction module is used for extracting a target region of the image based on the category and the shape of the circumscribed convex hull of each pixel;
the analysis module is configured to perform shape analysis on the target region to obtain an analysis result, where the analysis result includes any one of: meeting the regulation, not meeting the regulation;
and the early warning module is used for sending out an early warning signal if the analysis result does not accord with the regulation.
6. The system of claim 5, wherein the target region comprises any one of: a stain area, a channel area, a material area, a worker area; the analysis module further comprises a first analysis unit for:
and analyzing the shape of the dirty area, and if the area of the dirty area is larger than a preset area, obtaining an analysis result which does not accord with the regulation.
7. The system of claim 6, wherein the analysis module further comprises a second analysis unit to:
and analyzing the channel area or the material area, and if the placement of the materials is found in the channel area or a preset area in the material area, obtaining an analysis result which does not accord with the regulation.
8. The system of claim 6, wherein the analysis module further comprises a third analysis unit to:
and extracting workers in the worker area, analyzing the head state of the workers, and obtaining an analysis result which does not accord with the regulation if the workers are judged not to wear safety helmets.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 4 are implemented when the computer program is executed by the processor.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1-4.
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| CN114048953A (en) * | 2021-10-14 | 2022-02-15 | 王明康 | Intelligent wind control evaluation method based on multidimensional perception and enterprise data quantification |
| CN114048953B (en) * | 2021-10-14 | 2023-09-29 | 上海翼方键数科技有限公司 | Intelligent wind control evaluation method based on multidimensional sensing and enterprise data quantification |
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