CN112499017A - Garbage classification method and device and garbage can - Google Patents
Garbage classification method and device and garbage can Download PDFInfo
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- CN112499017A CN112499017A CN202011290703.4A CN202011290703A CN112499017A CN 112499017 A CN112499017 A CN 112499017A CN 202011290703 A CN202011290703 A CN 202011290703A CN 112499017 A CN112499017 A CN 112499017A
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 16
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 230000006399 behavior Effects 0.000 claims description 35
- 239000011521 glass Substances 0.000 claims description 6
- 239000002699 waste material Substances 0.000 claims description 5
- 238000005303 weighing Methods 0.000 claims description 5
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- 230000002123 temporal effect Effects 0.000 claims description 3
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- 238000007726 management method Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F1/00—Refuse receptacles; Accessories therefor
- B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F1/00—Refuse receptacles; Accessories therefor
- B65F1/14—Other constructional features; Accessories
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F2210/00—Equipment of refuse receptacles
- B65F2210/138—Identification means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/10—Waste collection, transportation, transfer or storage, e.g. segregated refuse collecting, electric or hybrid propulsion
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Abstract
The invention is suitable for the technical field of garbage classification, and provides a garbage classification method, a garbage classification device and a garbage can, wherein the method comprises the following steps: collecting a field image of a garbage delivery position; monitoring a garbage delivery behavior by adopting a video motion detection algorithm aiming at the field image; when a garbage delivery behavior is monitored, extracting a garbage image from the site image; and matching the garbage images in a preset garbage model library by adopting a target identification algorithm, and identifying the garbage categories corresponding to the garbage images. According to the method, after the site image of the garbage delivery position is collected, the garbage category corresponding to the garbage image can be automatically identified according to the site image, a user does not need to know the garbage category to which each garbage category belongs, and the garbage classification efficiency is greatly improved.
Description
Technical Field
The invention belongs to the technical field of garbage classification, and particularly relates to a garbage classification method, a garbage classification device and a garbage can.
Background
With the implementation of garbage classification management regulations, classification garbage boxes are distributed in many communities at present so as to realize garbage classification and delivery. However, although the nation advocates a garbage classification system and performs garbage classification actions in a plurality of cities, many communities have no people to monitor the delivery of garbage classification, or only use the site supervision of property personnel to realize the classified delivery of garbage, so that the garbage delivery efficiency is low, and a large amount of manpower and material resources are wasted.
Therefore, how to realize efficient garbage classification becomes an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a garbage classification method, a garbage classification device and a garbage can, and aims to solve the technical problem of poor garbage classification efficiency in the prior art.
In a first aspect, the present invention provides a garbage classification method, including:
collecting a field image of a garbage delivery position;
monitoring a garbage delivery behavior by adopting a video motion detection algorithm aiming at the field image;
when a garbage delivery behavior is monitored, extracting a garbage image from the site image;
and matching the garbage images in a preset garbage model library by adopting a target identification algorithm, and identifying the garbage categories corresponding to the garbage images.
Preferably, the step of monitoring the garbage delivery behavior by using a video motion detection algorithm for the live image includes:
extracting a plurality of adjacent image frames from the live image;
extracting a motion region from the adjacent image frames based on a temporal difference of pixels;
and judging whether a garbage delivery behavior exists according to the range of the motion area.
Preferably, the step of performing matching operation on the garbage image in a preset garbage model library by using a target recognition algorithm, and recognizing the garbage category corresponding to the garbage image includes:
matching the garbage images in a preset garbage model library by adopting a target identification algorithm to identify garbage types corresponding to the garbage images, wherein the garbage model library comprises garbage images such as clothes, glass and the like;
and determining the garbage category to which the garbage category belongs according to the garbage classification standard.
Preferably, the method further comprises:
respectively acquiring the volume and the weight of the garbage corresponding to the garbage image by adopting a scanning device and a weighing platform;
calculating the actual density of the garbage according to the volume and the weight;
and comparing the actual density with the preset density of the garbage types, and judging whether the garbage types are identified wrongly.
Preferably, the step of comparing the actual density with a preset density of the garbage category and determining whether the garbage category is identified incorrectly includes:
comparing the actual density with a preset density of the garbage category, and calculating a difference value between the actual density and the preset density;
and judging whether the difference value is within a preset threshold range, and if so, judging that the garbage type is not identified wrongly.
Preferably, the method further comprises:
if the difference value is not within the range of a preset threshold value, judging that the garbage type identification is wrong;
and carrying out voice prompt of the garbage category recognition error.
Preferably, after the step of performing matching operation on the garbage image in a preset garbage model library by using a target recognition algorithm and recognizing the garbage category corresponding to the garbage image, the method further includes:
controlling to open the door of the dustbin corresponding to the garbage category;
and when the garbage is detected to fall into the garbage can, voice prompt for completing garbage putting is carried out.
In a second aspect, the present invention provides a garbage classification apparatus, comprising:
the field image acquisition module is used for acquiring field images of the garbage delivery positions;
the delivery behavior monitoring module is used for monitoring the garbage delivery behavior by adopting a video mobile detection algorithm aiming at the field image;
the garbage image extraction module is used for extracting a garbage image from the site image when a garbage delivery behavior is monitored;
and the garbage category identification module is used for performing matching operation on the garbage images in a preset garbage model library by adopting a target identification algorithm to identify the garbage categories corresponding to the garbage images.
In a third aspect, the present invention further provides a garbage can, comprising:
a processor; and
a memory communicatively coupled to the processor; wherein,
the memory stores readable instructions which, when executed by the processor, implement the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed, performs the method of the first aspect.
According to the garbage classification method and the garbage can, after the site image of the garbage delivery position is collected, the garbage delivery behavior is monitored by adopting a video motion detection algorithm, when the garbage delivery behavior is monitored, the garbage image is extracted from the site image, and the garbage image is subjected to matching operation in the preset garbage model library by adopting a target recognition algorithm, so that the garbage category corresponding to the garbage image is automatically recognized, a user does not need to know the garbage category to which each garbage category belongs, the garbage can be automatically classified and put in, and the garbage classification efficiency is greatly improved.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a garbage classification method according to an embodiment.
Fig. 2 is a block diagram showing a configuration of the garbage sorting apparatus according to the second embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of specific implementations of the present invention is provided in conjunction with specific embodiments:
the first embodiment is as follows:
fig. 1 is a flowchart illustrating an implementation of a garbage classification method according to an embodiment. The garbage classification method shown in the first embodiment is applicable to a garbage can, and a processor is arranged in the garbage can to realize automatic classification and placement of garbage when a user places garbage. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and detailed as follows:
step S110, collecting the site image of the garbage delivery position.
And step S120, aiming at the field image, monitoring the garbage delivery behavior by adopting a video motion detection algorithm.
And step S130, when the garbage delivery behavior is monitored, extracting a garbage image from the site image.
And step S140, performing matching operation on the garbage images in a preset garbage model library by adopting a target identification algorithm, and identifying the garbage types corresponding to the garbage images.
In the exemplary embodiment, the garbage can is provided with the camera, and images of the garbage delivery positions are shot through the camera.
And after a field image shot for the garbage delivery position is acquired, monitoring the garbage delivery behavior according to the field image.
When the garbage delivery behavior is monitored according to the site image, whether the image characteristics of the garbage are included in the image characteristics can be judged by extracting the image characteristics in the site image, so that whether the garbage delivery behavior exists is judged; or monitoring the garbage delivery behavior by adopting a video motion detection algorithm; the method can also be used for monitoring the garbage delivery behaviors in other modes, and the modes for monitoring the garbage delivery behaviors are not described one by one.
When the video motion detection algorithm is adopted to monitor the garbage delivery behavior, a plurality of adjacent image frames can be extracted from a field image, then a motion area is extracted from the adjacent image frames based on the time difference of pixels, and finally whether the garbage delivery behavior exists or not is judged according to the range of the motion area.
For example, adjacent image frames A, B, C, D are extracted from the live image, the corresponding pixel values of the adjacent image frames are subtracted to obtain a difference image, then the difference image is binarized, and if the corresponding pixel value of the difference image changes to be less than a predetermined threshold value M, the difference image is regarded as a background pixel; if the corresponding pixel values of the differential image are changed greatly and exceed the set threshold value M, the regions are considered to be caused by moving objects in the image and are marked as foreground pixels, and finally the positions of the moving objects in the image can be determined by utilizing the marked pixel regions, so that the fact that the delivery behavior exists above the box body is judged.
When the garbage category corresponding to the garbage image is identified by adopting a target identification algorithm, the garbage image is subjected to matching operation in a preset garbage model library by adopting the target identification algorithm to identify the garbage category corresponding to the garbage image (the garbage model library comprises the garbage images such as clothes and glass), and then the garbage category to which the garbage category belongs is determined according to a garbage classification standard.
The garbage model library is generated by performing deep learning on garbage images of various garbage types in advance. And establishing a garbage model library of various garbage types such as clothes, tablecloth, washcloths, schoolbag, shoes, glass bottles, broken glass pieces, thermos bottles and the like through deep learning algorithms such as a convolutional neural network and the like to form a garbage model library.
The national garbage classification standard contains garbage categories to which the garbage categories belong, so that after the garbage categories corresponding to the garbage images are identified, the garbage categories to which the garbage categories belong can be determined according to the garbage classification standard.
Therefore, after the site images of the garbage delivery positions are collected, the garbage delivery behaviors are monitored by adopting a video motion detection algorithm, when the garbage delivery behaviors are monitored, the garbage images are extracted from the site images, and the garbage images are subjected to matching operation in a preset garbage model library by adopting a target identification algorithm, so that the garbage classes corresponding to the garbage images are automatically identified, the garbage classification can be automatically realized without the user knowing the garbage classes to which the garbage classes belong, and the garbage classification efficiency is greatly improved.
Optionally, after identifying the garbage type according to the field image, respectively acquiring the volume and the weight of the garbage corresponding to the garbage image by using a scanning device and a weighing platform, and calculating the actual density of the garbage according to the volume and the weight; and comparing the actual density with the preset density of the garbage types to judge whether the garbage types are identified wrongly.
It should be noted that each waste type is preset with a corresponding preset density, which is the conventional density of a certain waste type.
For example, a laser three-dimensional scanning device is adopted to scan the volume of the garbage to be V in real time, and meanwhile, an automatic weighing platform is adopted to measure the weight G of the garbage; after the volume and the weight are obtained, the actual garbage density P1 is calculated; since the image-identified trash type is article a, for example, article a is paper, the preset density (i.e., the conventional density) of the paper is P2, comparing the actual trash densities P1 and P2, and if the difference between the two densities is smaller than the threshold H, determining that the trash type is not identified incorrectly, that is, the trash type is article a; if the difference value of the two is not less than the threshold value H, the garbage type recognition is judged to be wrong, namely the garbage type is not the article A, at the moment, voice prompt of the garbage type recognition error is carried out, a supervisor is prompted to manually judge whether the garbage box bodies corresponding to the garbage type and the garbage type are consistent, and the user is guided to accurately deliver the garbage to the corresponding garbage box bodies.
After the garbage categories are identified, the door of the garbage can corresponding to the garbage categories is controlled to be opened, so that a user can conveniently and accurately deliver the garbage to the corresponding garbage can, and the classified putting of the garbage is efficiently and accurately realized.
Optionally, when detecting that the garbage falls into the garbage can, a voice prompt of completion of garbage throwing is performed to inform that delivery is completed.
Optionally, after the garbage delivery is completed, images in the garbage can body can be collected to judge whether the garbage can body is full, and when the garbage can body is full, a prompt for garbage cleaning is given, so that garbage management personnel can conveniently clean garbage, and the garbage management efficiency is greatly improved.
Example two:
as shown in fig. 2, a second embodiment of the present invention provides a garbage classification apparatus, which may perform all or part of the steps of any of the above-described garbage classification methods. The system comprises:
the field image acquisition module 1 is used for acquiring field images of the garbage delivery positions;
the delivery behavior monitoring module 2 is used for monitoring the garbage delivery behavior by adopting a video mobile detection algorithm aiming at the field image;
the rubbish image extraction module 3 is used for extracting rubbish images from the site images when rubbish delivery behaviors are monitored;
and the garbage category identification module 4 is used for performing matching operation on the garbage images in a preset garbage model library by adopting a target identification algorithm to identify the garbage categories corresponding to the garbage images.
Specifically, the delivery behavior monitoring module 2 includes:
an image frame extracting unit 21 for extracting a plurality of adjacent image frames from the live image;
a motion region extraction unit 22 for extracting a motion region from adjacent image frames based on a temporal difference of pixels;
and the garbage delivery behavior judging unit 23 is configured to judge whether a garbage delivery behavior exists according to the range of the motion area.
Specifically, the garbage category identifying module 4 includes:
a garbage type identifying unit 41, configured to perform matching operation on the garbage image in a preset garbage model library by using a target identification algorithm, and identify a garbage type corresponding to the garbage image, where the garbage model library includes garbage images such as clothes and glass;
and a garbage category determining unit 42, configured to determine a garbage category to which the garbage category belongs according to the garbage classification standard.
Specifically, the device further comprises:
the volume and weight acquisition module 5 is used for respectively acquiring the volume and weight of the garbage corresponding to the garbage image by adopting a scanning device and a weighing platform;
the actual density calculation module 6 is used for calculating the actual density of the garbage according to the volume and the weight;
and the density comparison module 7 is used for comparing the actual density with the preset density of the garbage types and judging whether the garbage types are identified wrongly.
Example three:
the third embodiment of the invention provides a garbage can, which can execute all or part of the steps of any one of the garbage classification methods. This dustbin includes:
a processor; and
a memory communicatively coupled to the processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of the above exemplary embodiments, which will not be described in detail herein.
In this embodiment, a storage medium is also provided, which is a computer-readable storage medium, such as a transitory and non-transitory computer-readable storage medium including instructions. The storage medium, for instance, includes a memory of instructions executable by a processor of the server system to perform the above-described garbage classification method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A garbage classification method is applied to a garbage can, and is characterized by comprising the following steps:
collecting a field image of a garbage delivery position;
monitoring a garbage delivery behavior by adopting a video motion detection algorithm aiming at the field image;
when a garbage delivery behavior is monitored, extracting a garbage image from the site image;
and matching the garbage images in a preset garbage model library by adopting a target identification algorithm, and identifying the garbage categories corresponding to the garbage images.
2. The method of claim 1, wherein the step of using a video motion detection algorithm to monitor spam behavior with respect to the live images comprises:
extracting a plurality of adjacent image frames from the live image;
extracting a motion region from the adjacent image frames based on a temporal difference of pixels;
and judging whether a garbage delivery behavior exists according to the range of the motion area.
3. The method of claim 1, wherein the garbage image is matched in a preset garbage model library by using a target recognition algorithm, and the step of recognizing the garbage category corresponding to the garbage image comprises:
matching the garbage images in a preset garbage model library by adopting a target identification algorithm to identify garbage types corresponding to the garbage images, wherein the garbage model library comprises garbage images such as clothes, glass and the like;
and determining the garbage category to which the garbage category belongs according to the garbage classification standard.
4. The method of claim 3, wherein the method further comprises:
respectively acquiring the volume and the weight of the garbage corresponding to the garbage image by adopting a scanning device and a weighing platform;
calculating the actual density of the garbage according to the volume and the weight;
and comparing the actual density with the preset density of the garbage types, and judging whether the garbage types are identified wrongly.
5. The method of claim 4, wherein the step of comparing the actual density with the preset density of the garbage category and determining whether the garbage category is identified as erroneous comprises:
comparing the actual density with a preset density of the garbage category, and calculating a difference value between the actual density and the preset density;
and judging whether the difference value is within a preset threshold range, and if so, judging that the garbage type is not identified wrongly.
6. The method of claim 5, wherein the method further comprises:
if the difference value is not within the range of a preset threshold value, judging that the garbage type identification is wrong;
and carrying out voice prompt of the garbage category recognition error.
7. The method of claim 1, wherein after the step of matching the spam image in a pre-set spam model library using a target recognition algorithm to identify a corresponding spam category of the spam image, the method further comprises:
controlling to open the door of the dustbin corresponding to the garbage category;
and when the garbage is detected to fall into the garbage can, voice prompt for completing garbage putting is carried out.
8. A waste sorting device, characterized in that the device comprises:
the field image acquisition module is used for acquiring field images of the garbage delivery positions;
the delivery behavior monitoring module is used for monitoring the garbage delivery behavior by adopting a video mobile detection algorithm aiming at the field image;
the garbage image extraction module is used for extracting a garbage image from the site image when a garbage delivery behavior is monitored;
and the garbage category identification module is used for performing matching operation on the garbage images in a preset garbage model library by adopting a target identification algorithm to identify the garbage categories corresponding to the garbage images.
9. A waste bin, the waste bin comprising:
a processor; and
a memory communicatively coupled to the processor; wherein,
the memory stores readable instructions which, when executed by the processor, implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any one of claims 1-7.
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