CN116824453B - Garbage dumping behavior identification method, device, electronic equipment and readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a garbage dumping behavior identification method, a garbage dumping behavior identification device, electronic equipment and a readable medium. One embodiment of the method comprises the following steps: in response to the refuse transfer vehicle being driven into the vehicle weighing position, weighing the refuse transfer vehicle by the target wagon balance; reading vehicle identity information of the refuse transfer vehicle through a vehicle identity card reader; in response to successful reading, indicating the garbage transfer vehicle to dump garbage; in response to the dumping being completed, weighing the garbage transfer vehicle by the target wagon balance for the second time; performing dumping behavior recognition on the target video to generate initial dumping behavior recognition information; and generating dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial dumping behavior identification information. This embodiment confirms rubbish transfer vehicle's rubbish volume of dumping effectively, can also carry out accurate traceability to the rubbish source simultaneously to and the accurate detection to the illegal action of dumping.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a garbage dumping behavior identification method, a garbage dumping behavior identification device, an electronic device, and a readable medium.
Background
The garbage transfer station is a station for temporarily storing garbage collected in a city so as to realize the purpose of garbage concentration. Currently, when garbage disposal is performed, the following methods are generally adopted: the refuse transfer vehicle is driven into the refuse transfer station to dump the transported refuse into a refuse collection device (e.g., refuse collection bin).
However, the inventors found that when the above manner is adopted, there are often the following technical problems:
firstly, the dumping amount of garbage of each garbage transfer vehicle cannot be accurately determined, and the garbage source is accurately traced;
secondly, the compliance of the dumping behavior of the garbage can not be effectively identified, so that when the illegal dumping behavior occurs, accurate tracing can not be performed;
third, when the dumped garbage is not completely dumped to the garbage collection device, the surrounding environment is affected.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a garbage dumping behavior identification method, apparatus, electronic device, and readable medium to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a garbage dumping behavior identification method, the method comprising: in response to a refuse transfer vehicle driving into a vehicle weighing position, performing vehicle weighing on the refuse transfer vehicle through a target wagon balance to generate first vehicle weighing information, wherein the target wagon balance is installed at the vehicle weighing position; reading the vehicle identity information of the garbage transfer vehicle through a vehicle identity card reader; in response to successful reading, the garbage transfer vehicle is instructed to dump garbage; in response to the dumping, carrying out secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance so as to generate second vehicle weighing information; performing dumping behavior recognition on a target video to generate initial dumping behavior recognition information, wherein acquisition time corresponding to a start frame image included in the target video is time when the refuse transfer vehicle drives into the vehicle weighing position, and acquisition time corresponding to an end frame image included in the target video is time when the refuse transfer vehicle drives out of a refuse transfer station; generating toppling behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial toppling behavior identification information, wherein the toppling behavior identification information comprises: dumping quantity of garbage and dumping behavior information.
In a second aspect, some embodiments of the present disclosure provide a garbage dumping behavior recognition device, the device comprising: a first vehicle weighing unit configured to perform vehicle weighing on a refuse transfer vehicle by a target wagon balance in response to the refuse transfer vehicle being driven into a vehicle weighing position, to generate first vehicle weighing information, wherein the target wagon balance is mounted at the vehicle weighing position; a reading unit configured to read vehicle identification information of the refuse transfer vehicle through a vehicle identification card reader; an instruction unit configured to instruct the refuse transfer vehicle to dump refuse in response to a successful reading; a second vehicle weighing unit configured to perform secondary vehicle weighing on the refuse transfer vehicle through the target wagon balance in response to the dumping being completed, so as to generate second vehicle weighing information; the dumping behavior recognition unit is configured to recognize dumping behavior of a target video to generate initial dumping behavior recognition information, wherein acquisition time corresponding to a start frame image included in the target video is time when the garbage transfer vehicle is driven into the vehicle weighing position, and acquisition time corresponding to an end frame image included in the target video is time when the garbage transfer vehicle is driven out of the garbage transfer station; a generation unit configured to generate toppling behavior identification information based on the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial toppling behavior identification information, wherein the toppling behavior identification information includes: dumping quantity of garbage and dumping behavior information.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantageous effects: through the garbage dumping behavior identification method of some embodiments of the disclosure, the garbage dumping amount of the garbage transfer vehicle can be effectively determined, and meanwhile, the garbage source can be accurately traced, and the illegal dumping behavior can be accurately detected. Specifically, the reason that the garbage dumping amount cannot be determined, the garbage source is traced, and the illegal dumping behavior is detected is that: firstly, the dumping amount of garbage of each garbage transfer vehicle cannot be accurately determined, and the garbage source is accurately traced; second, the compliance of the dumping behavior of the garbage cannot be effectively identified, so that accurate traceability cannot be performed when the dumping behavior is illegal. Based on this, the refuse dumping behavior recognition method of some embodiments of the present disclosure first performs vehicle weighing on a refuse transfer vehicle by a target wagon balance in response to the refuse transfer vehicle driving into a vehicle weighing position, to generate first vehicle weighing information, wherein the target wagon balance is mounted at the vehicle weighing position. The weight sum of the vehicle dead weight and the garbage weight is obtained. And then, reading the vehicle identity information of the garbage transfer vehicle through a vehicle identity card reader. Because the garbage transfer vehicle often corresponds to a fixed garbage collection area, accurate tracing to garbage sources can be achieved by obtaining vehicle identity information. Further, in response to the successful reading, the refuse transfer vehicle is instructed to dump refuse. And then, in response to the dumping, carrying out secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance so as to generate second vehicle weighing information. Thus, the dead weight of the vehicle is obtained. In addition, the dumping behavior recognition is performed on the target video to generate initial dumping behavior recognition information, wherein the acquisition time corresponding to the initial frame image included in the target video is the time when the garbage transfer vehicle is driven into the vehicle weighing position, and the acquisition time corresponding to the end frame image included in the target video is the time when the garbage transfer vehicle is driven out of the garbage transfer station. By combining the videos, the detection of the dumping behavior is realized. Finally, generating dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial dumping behavior identification information, wherein the dumping behavior identification information comprises: dumping quantity of garbage and dumping behavior information. Through the mode, the garbage dumping quantity of the garbage transfer vehicle can be effectively determined, and meanwhile, the garbage source can be accurately traced, and the illegal dumping behavior can be accurately detected.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a garbage dumping behavior identification method in accordance with the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of a garbage dumping behavior identification device according to the disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a flow 100 of some embodiments of a garbage dumping behavior identification method in accordance with the present disclosure is shown. The garbage dumping behavior identification method comprises the following steps:
In response to the refuse transfer vehicle being driven into the vehicle weighing location, the refuse transfer vehicle is vehicle weighed by the target wagon balance to generate first vehicle weighing information, step 101.
In some embodiments, in response to the refuse transfer vehicle being driven into the vehicle weighing location, an executing body (e.g., computing device) of the refuse dump behavior recognition method may vehicle weigh the refuse transfer vehicle via the target wagon balance to generate the first vehicle weighing information. Wherein, the target wagon balance is installed in the above-mentioned vehicle weighing position. Wherein the refuse transfer vehicle is a vehicle for transferring refuse to a refuse transfer station. The first vehicle information weighing information may characterize the sum of the vehicle deadweight of the refuse transfer vehicle and the weight of the refuse carried. For example, the first vehicle weighing information may be "15 tons".
The computing device may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein. It should be appreciated that the number of computing devices may have any number, as desired for implementation.
In some optional implementations of some embodiments, before the vehicle weighing the refuse transfer vehicle by the target wagon balance in response to the refuse transfer vehicle driving into the vehicle weighing location to generate the first vehicle weighing information, the method further includes:
in the first step, the target camera is controlled to acquire images in response to the fact that the time length of the target infrared light shielded is longer than the preset time length, and then the target images are obtained.
Wherein the target infrared light is an infrared light emitted by an intrusion detector. The intrusion detector and the target camera are arranged at the entrance of the garbage transfer station. In practice, as the length of the body of the refuse transfer vehicle is long, whether the refuse transfer vehicle is driven into the refuse transfer station or not can be determined by determining the time period for which the infrared light emitted by the intrusion detector is blocked. The target image can be a picture image acquired at the entrance of the garbage transfer station and acquired by the target camera. Specifically, for example, the target camera may be in a closed state, and when the duration of the target infrared light being blocked is greater than the preset duration, the execution body may control the target camera to be turned on and perform image acquisition. For another example, the target camera may be in an on state, and when the duration of the target infrared light being blocked is greater than the preset duration, the current frame image in the video collected by the target camera may be determined to be the target image.
And secondly, carrying out object recognition on the target image to generate recognition object information.
Wherein the identification object information includes: a primary object class and a secondary object class. Wherein the primary object class characterizes a coarse classification class of objects included in the target image. The secondary object class characterizes a subdivision class of the object included in the target image. In practice, the execution subject may perform object recognition on the target image through an object recognition model to generate recognition object information. The main network of the object recognition model adopts a YOLO (You Only Look Once) model, and the object recognition model comprises two classifiers which are connected with the main network to generate a primary object class and a secondary object class.
And thirdly, generating vehicle guiding information according to the current position of the garbage transfer vehicle and the weighing position of the vehicle in response to the fact that the primary object type is determined to be the vehicle type and the secondary object type is determined to be the garbage transfer vehicle type.
The vehicle guiding information is information for guiding the refuse transfer vehicle to drive into the weighing position of the vehicle. The vehicle guidance information includes: and guiding the route. In practice, the guiding route is the route from the refuse transfer vehicle to the center of the weighing position of the vehicle. Specifically, the execution subject may generate the guiding route in a curve fitting manner.
Fourth, the vehicle guidance information is displayed on a vehicle guidance information display device.
The vehicle guiding information display device is arranged in the garbage transfer station and faces to a cockpit of the garbage transfer vehicle, so as to be used for guiding the garbage transfer vehicle to drive into the vehicle weighing position. In practice, the vehicle guidance information display device may be a display screen.
Step 102, reading vehicle identity information of the refuse transfer vehicle through a vehicle identity card reader.
In some embodiments, the executing entity may read the vehicle identity information of the refuse transfer vehicle through a vehicle identity card reader. The vehicle identity card reader may be an RFID (Radio Frequency Identification ) card reader. The vehicle identity information may characterize a vehicle identity of the refuse transfer vehicle. In practice, the vehicle identity information may include, but is not limited to, at least one of: vehicle type, license plate number, vehicle-associated institution, garbage collection area.
As an example, the driver of the refuse transfer vehicle may read, in a handheld manner, the identity card containing the vehicle identity information of the refuse transfer vehicle through the vehicle identity card reader, to obtain the vehicle identity information.
As yet another example, the identity card of the refuse transfer vehicle may be fixed to the refuse transfer vehicle, and when the refuse transfer vehicle travels within a signal range of the vehicle identity card reader, the reading of the vehicle identity information may be achieved.
And step 103, in response to the successful reading, indicating the garbage transfer vehicle to dump garbage.
In some embodiments, the executing entity may instruct the refuse transfer vehicle to dump refuse in response to a successful read. In practice, the executing body may instruct the refuse transfer vehicle to dump refuse into a refuse collection device within a refuse transfer station. Specifically, the garbage collection device may be a garbage collection bin, and the garbage collection device includes a bin gate, and when dumping of garbage is not required, the bin gate of the garbage collection device is in a closed state. The garbage collection bin may be separated from the garbage transfer station to enable further garbage transfer.
As an example, when the driver can read the identity card containing the vehicle identity information of the refuse transfer vehicle through the vehicle identity card reader in a handheld manner and the reading is successful, the executing body may prompt the driver to control the refuse transfer vehicle to dump refuse in a voice broadcast manner through the vehicle identity card reader.
As yet another example, when the vehicle identity card reader successfully reads the identity card fixed on the refuse transfer vehicle, the executing body may control a voice broadcasting device in the refuse transfer station to prompt the refuse transfer vehicle to dump refuse.
And 104, in response to the dumping, performing secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance to generate second vehicle weighing information.
In some embodiments, in response to dumping being completed, the executing body may perform secondary vehicle weighing on the refuse transfer vehicle through the target wagon balance to generate second vehicle weighing information. The second vehicle weighing information represents the weight of the refuse transfer vehicle in an empty state. For example, the second vehicle weighing information may be "10" tons.
Step 105, performing dumping behavior recognition on the target video to generate initial dumping behavior recognition information.
In some embodiments, the executing entity may perform dumping behavior recognition on the target video to generate initial dumping behavior recognition information. The acquisition time corresponding to the initial frame image included in the target video is the time when the refuse transfer vehicle is driven into the weighing position of the vehicle. And the acquisition time corresponding to the ending frame image included in the target video is the time when the garbage transfer vehicle drives away from the garbage transfer station. In practice, the target video may be video captured towards a camera of the above-described garbage collection device. The initial dumping behavior identification information may characterize the dumping behavior of the refuse transfer vehicle. Specifically, the initial pouring behavior identification information may include: the garbage dumping start time, the garbage dumping end time, the primary card punching time and the secondary card punching time. Wherein, the start time of dumping of the garbage characterizes the time when the transfer vehicle of the garbage station starts dumping of the garbage. The end time of dumping the garbage characterizes the time when the garbage station transfer vehicle ends dumping the garbage. The time of once punching card represents the time that the rubbish transfer vehicle carries out identity reading through the vehicle identity card punching device for the first time. The secondary card punching time characterizes the time of identity reading of the garbage transfer vehicle after garbage dumping is finished and through the vehicle identity card punch for the second time. In practice, the execution main body can respectively determine the garbage dumping start time, the garbage dumping end time, the primary card punching time and the secondary card punching time through a behavior recognition model. Specifically, the behavior recognition model may be a YOLO model.
In some optional implementations of some embodiments, the performing body performs pouring behavior recognition on the target video to generate initial pouring behavior recognition information, and may include the following steps:
and firstly, performing reverse frame image positioning on the target video to determine a target frame image.
The target frame image is an image of the refuse transfer vehicle after the refuse dumping is finished. In practice, first, the executing body may perform playback on the target video to obtain a playback video. Then, the execution subject may input the inverted video into the behavior recognition model to determine a frame number including an image of the refuse transfer vehicle after the end of dumping the refuse. Then, the execution subject may determine an image corresponding to the frame number as the target frame image.
And secondly, carrying out video segmentation on the target video by using the target frame image to obtain a first video and a second video.
Wherein, the frame number of the ending frame image included in the first video is smaller than the frame number of the starting frame image included in the second video.
And thirdly, carrying out similar frame image rejection on the first video to obtain a video after frame image rejection.
In practice, the executing body may sequentially determine the image similarity between every two adjacent frame images in the first video, and delete the next frame image in the two adjacent frame images from the first video when the image similarity is greater than a preset threshold.
And fourthly, performing dumping behavior recognition on the video after the frame image is removed through a pre-trained dumping behavior recognition model included by the cloud server so as to generate initial dumping behavior recognition information.
In practice, the executing body may send the video after the frame image is removed to the cloud server, and the dumping behavior recognition model performs dumping behavior recognition on the video after the frame image is removed to generate the initial dumping behavior recognition information. Specifically, the dumping behavior recognition model may be a YOLO model. In practice, as the volume of the computing resources in the garbage transfer station is smaller, a cloud (cloud server) architecture of the edge (garbage transfer station) is adopted to identify the dumping behavior of the video after the frame images are removed.
In some optional implementations of some embodiments, after the performing the pouring behavior recognition on the target video to generate the initial pouring behavior recognition information, the method further includes:
Firstly, determining a garbage residual area through a pre-trained garbage residual area identification model and the second video, wherein the garbage residual area identification model is included by the cloud server.
The garbage residue area identification model comprises 20 serially connected convolution layers and 1 fully connected layer. The output of the garbage residue region identification model is an image in which the garbage residue region is framed.
And secondly, determining a candidate frame image sequence through the cloud server.
The candidate frame images in the candidate frame image sequence are frame images in which the garbage residual area included in the second video is not blocked.
In practice, for each frame image in the second video, the execution subject may perform image difference processing on the frame image and the reference image to generate an image difference score, and determine the frame image as a candidate frame image when the image difference value is greater than a preset image difference score.
And thirdly, determining a residual garbage information set through a pre-trained residual garbage identification model and the candidate frame image sequence, wherein the residual garbage identification model and the candidate frame image sequence are included by the cloud server.
Wherein, the residual junk information in the residual junk information set comprises: residual trash location information and residual trash category. The main network of the residual garbage identification model adopts a garbage residual region identification model, and 1 full connection layer is added for generating the residual garbage category. Specifically, in the model training stage, the execution body can train the garbage residue region identification model preferentially, and train the residue garbage identification model on the basis of the garbage residue region identification model after the garbage residue region identification model is trained, so that the calculation resource consumption in the training stage is reduced.
And step four, dividing the residual garbage information set according to the residual garbage category included in the residual garbage information to obtain a first residual garbage information set and a second residual garbage information set.
The first residual garbage information comprises a non-liquid residual garbage type, and the second residual garbage information comprises a liquid residual garbage type.
And fifthly, pushing the garbage at the position corresponding to the position information of the residual garbage included in the first residual garbage information into a garbage collection device through a pushing device for each piece of the first residual garbage information in the first residual garbage information set. Wherein the pushing device is parallel to the ground so as to push the non-liquid garbage into the garbage collection device. The pushing device is driven by an air pump. In practice, the execution body may control the air pump to push the pushing device to push the garbage at the position corresponding to the position information of the residual garbage included in the first residual garbage information into the garbage collection device.
And sixthly, flushing the corresponding position of the residual garbage position information included in the second residual garbage information by a flushing device for each second residual garbage information in the second residual garbage information set.
Wherein the flushing device faces the edge of the garbage collection device and is used for flushing liquid residual garbage around the residual garbage position into the garbage collection device. In practice, the flushing device may be a high pressure flushing device.
The content of "in some alternative implementations of some embodiments" in step 105, as an inventive point of the present disclosure, solves the technical problem three mentioned in the background art, namely "when the dumped garbage is not completely dumped to the garbage collection device, it may have an influence on the surrounding environment". In practice, when the refuse transfer vehicle is dumping refuse into the refuse collection device, refuse remains around the refuse collection device are extremely liable to occur. Because the garbage transfer station often needs to temporarily store the collected garbage, the garbage in the garbage transfer station can generate a large amount of malodorous gas along with the increase of temperature and the time, so that the surrounding environment is influenced. The garbage collection device comprises the bin gate, so that the malodorous gas can be prevented from overflowing to a certain extent. When the residual garbage around the garbage collection device is not cleaned in time, the malodorous gas can influence the environment around the garbage transfer station. Accordingly, the present disclosure first locates the garbage residue region by the garbage residue region identification model. Then, the candidate frame image sequence is determined through the cloud server. Because the garbage transfer vehicle can shelter from the garbage residual area, the positioning accuracy of the position of the residual garbage is affected, and therefore, frame images (candidate frame images) which are included in the second video and are not sheltered in the garbage residual area are screened out for subsequent accurate residual garbage positioning. Further, determining a set of residual garbage information through a pre-trained residual garbage identification model and the candidate frame image sequence, wherein the residual garbage information in the set of residual garbage information comprises: residual trash location information and residual trash category. In addition, the residual garbage information set is divided according to the residual garbage types included in the residual garbage information to obtain a first residual garbage information set and a second residual garbage information set, wherein the residual garbage types included in the first residual garbage information are non-liquid residual garbage types, and the residual garbage types included in the second partial residual garbage information are liquid residual garbage types. In addition, for each piece of first residual garbage information in the first residual garbage information set, pushing garbage at a position corresponding to residual garbage position information included in the first residual garbage information into a garbage collection device by a pushing device. And finally, flushing the corresponding position of the residual garbage position information included in the second residual garbage information by a flushing device for each second residual garbage information in the second residual garbage information set. And carrying out accurate positioning and classification of the residual garbage through the residual garbage identification model. Considering that the residual waste is mainly classified into two main categories, namely, a liquid residual waste category and a non-liquid residual waste category. The liquid residual garbage category is often attached to the ground and is difficult to clean up by the pushing device. Therefore, the garbage collection device is pushed with non-liquid residual garbage (garbage corresponding to the first residual garbage information) through the pushing device, and the position corresponding to the liquid residual garbage (garbage corresponding to the second residual garbage information) is flushed through the flushing device, so that the garbage collection device is cleaned of surrounding garbage, and the influence of generated malodorous gas on the surrounding environment is avoided.
And 106, generating dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial dumping behavior identification information.
In some embodiments, the executing entity may generate the toppling behavior identification information based on the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial toppling behavior identification information. Wherein the pouring behavior identification information includes: dumping quantity of garbage and dumping behavior information. In practice, the executing body may determine a difference between the second vehicle weighing information and the first vehicle weighing information as the garbage dumping amount.
In some optional implementations of some embodiments, the executing entity may generate the dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial dumping behavior identification information, and may include the steps of:
and a first step of determining a difference between the second vehicle weighing information and the first vehicle weighing information as the garbage dumping amount.
And a second step of generating the dumping behavior information according to the vehicle identity information and the initial dumping behavior identification information.
In practice, when the vehicle identity information is not empty, the primary card punching time and the secondary card punching time included in the initial dumping behavior identification information are not empty, and the garbage dumping amount is not negative, determining the garbage dumping compliance information as the dumping behavior information. And when the vehicle identity information is empty, or the primary card punching time or the secondary card punching time included in the initial dumping behavior identification information is empty, or the garbage dumping quantity is negative, determining the garbage dumping illegal information as the dumping behavior information.
Optionally, the method further comprises:
in response to determining that the dumping behavior information characterizes that the refuse transfer vehicle has not been subjected to secondary identity reading by the vehicle identity card reader after dumping the refuse, performing the following processing steps:
in response to determining that the vehicle identity information is in a target gray list, deleting the vehicle identity information from the target gray list, and adding the vehicle identity information to a target black list.
The target gray list may be a list including vehicle identity information of 1-time garbage violation dumping behavior. The target blacklist may be a list of vehicle identity information including at least 2 spam-violations. In particular, the target gray list and the target black list may be maintained in common by a plurality of waste transfer stations.
And a second step of adding the vehicle identity information to the target gray list in response to determining that the vehicle identity information is not in the target gray list and is not in the target black list.
And thirdly, generating dumping behavior abnormality prompt information.
Wherein, the unusual prompt message includes: abnormal pouring behavior occurrence time, abnormal pouring behavior occurrence place and list category. In practice, the site where the abnormal dumping action occurs may include a waste terminal name and a waste terminal address. The list category may characterize a list category to which the refuse transfer vehicle belongs, e.g., a target blacklist or a target gray list.
As an example, the anomaly prompt message may be: { abnormal dumping behavior occurrence time: 2023, 5, 20, 9:37 minutes; abnormal dumping behavior occurs at the site: XX street XX garbage transfer station in XX area; list category: target blacklist }.
Optionally, the method further comprises:
and controlling a spraying device in the garbage transfer station to spray disinfectant in response to determining that the garbage transfer vehicle drives away from the garbage transfer station.
Wherein, spray set sets up in the inside top of above-mentioned rubbish transfer station for spray disinfectant.
The above embodiments of the present disclosure have the following advantageous effects: through the garbage dumping behavior identification method of some embodiments of the disclosure, the garbage dumping amount of the garbage transfer vehicle can be effectively determined, and meanwhile, the garbage source can be accurately traced, and the illegal dumping behavior can be accurately detected. Specifically, the reason that the garbage dumping amount cannot be determined, the garbage source is traced, and the illegal dumping behavior is detected is that: firstly, the dumping amount of garbage of each garbage transfer vehicle cannot be accurately determined, and the garbage source is accurately traced; second, the compliance of the dumping behavior of the garbage cannot be effectively identified, so that accurate traceability cannot be performed when the dumping behavior is illegal. Based on this, the refuse dumping behavior recognition method of some embodiments of the present disclosure first performs vehicle weighing on a refuse transfer vehicle by a target wagon balance in response to the refuse transfer vehicle driving into a vehicle weighing position, to generate first vehicle weighing information, wherein the target wagon balance is mounted at the vehicle weighing position. The weight sum of the vehicle dead weight and the garbage weight is obtained. And then, reading the vehicle identity information of the garbage transfer vehicle through a vehicle identity card reader. Because the garbage transfer vehicle often corresponds to a fixed garbage collection area, accurate tracing to garbage sources can be achieved by obtaining vehicle identity information. Further, in response to the successful reading, the refuse transfer vehicle is instructed to dump refuse. And then, in response to the dumping, carrying out secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance so as to generate second vehicle weighing information. Thus, the dead weight of the vehicle is obtained. In addition, the dumping behavior recognition is performed on the target video to generate initial dumping behavior recognition information, wherein the acquisition time corresponding to the initial frame image included in the target video is the time when the garbage transfer vehicle is driven into the vehicle weighing position, and the acquisition time corresponding to the end frame image included in the target video is the time when the garbage transfer vehicle is driven out of the garbage transfer station. By combining the videos, the detection of the dumping behavior is realized. Finally, generating dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial dumping behavior identification information, wherein the dumping behavior identification information comprises: dumping quantity of garbage and dumping behavior information. Through the mode, the garbage dumping quantity of the garbage transfer vehicle can be effectively determined, and meanwhile, the garbage source can be accurately traced, and the illegal dumping behavior can be accurately detected.
With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a garbage dumping behavior recognition device, which correspond to those method embodiments shown in fig. 1, and which are particularly applicable in various electronic apparatuses.
As shown in fig. 2, the garbage dumping behavior recognition device 200 of some embodiments includes: a first vehicle weighing unit 201, a reading unit 202, an indicating unit 203, a second vehicle weighing unit 204, a toppling behavior identifying unit 205, and a generating unit 206. Wherein, the first vehicle weighing unit 201 is configured to perform vehicle weighing on the refuse transfer vehicle through a target wagon balance in response to the refuse transfer vehicle driving into a vehicle weighing position, so as to generate first vehicle weighing information, wherein the target wagon balance is mounted at the vehicle weighing position; a reading unit 202 configured to read vehicle identification information of the refuse transfer vehicle through a vehicle identification card reader; an instruction unit 203 configured to instruct the refuse transfer vehicle to dump refuse in response to a successful reading; a second vehicle weighing unit 204 configured to perform secondary vehicle weighing on the refuse transfer vehicle through the target wagon balance in response to the dumping being completed, to generate second vehicle weighing information; the dumping behavior recognition unit 205 is configured to perform dumping behavior recognition on a target video to generate initial dumping behavior recognition information, where an acquisition time corresponding to a start frame image included in the target video is a time when the garbage transfer vehicle is driven into the vehicle weighing position, and an acquisition time corresponding to an end frame image included in the target video is a time when the garbage transfer vehicle is driven out of the garbage transfer station; a generating unit 206 configured to generate dumping behavior identification information based on the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial dumping behavior identification information, wherein the dumping behavior identification information includes: dumping quantity of garbage and dumping behavior information.
It will be appreciated that the elements described in the dumping behaviour recognition device 200 correspond to the various steps in the method described with reference to figure 1. Thus, the operations, features and advantages described above with respect to the method are equally applicable to the garbage dumping behavior recognition device 200 and the units contained therein, and are not described here again.
Referring now to fig. 3, a schematic diagram of an electronic device (e.g., computing device) 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with programs stored in a read-only memory 302 or programs loaded from a storage 308 into a random access memory 303. In the random access memory 303, various programs and data necessary for the operation of the electronic device 300 are also stored. The processing means 301, the read only memory 302 and the random access memory 303 are connected to each other by a bus 304. An input/output interface 305 is also connected to the bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from read only memory 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to a refuse transfer vehicle driving into a vehicle weighing position, performing vehicle weighing on the refuse transfer vehicle through a target wagon balance to generate first vehicle weighing information, wherein the target wagon balance is installed at the vehicle weighing position; reading the vehicle identity information of the garbage transfer vehicle through a vehicle identity card reader; in response to successful reading, the garbage transfer vehicle is instructed to dump garbage; in response to the dumping, carrying out secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance so as to generate second vehicle weighing information; performing dumping behavior recognition on a target video to generate initial dumping behavior recognition information, wherein acquisition time corresponding to a start frame image included in the target video is time when the refuse transfer vehicle drives into the vehicle weighing position, and acquisition time corresponding to an end frame image included in the target video is time when the refuse transfer vehicle drives out of a refuse transfer station; generating toppling behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial toppling behavior identification information, wherein the toppling behavior identification information comprises: dumping quantity of garbage and dumping behavior information.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first vehicle weighing unit, a reading unit, an indicating unit, a second vehicle weighing unit, a toppling behavior recognition unit, and a generating unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the reading unit may also be described as "a unit that reads the vehicle identification information of the refuse transfer vehicle described above through a vehicle identification card reader".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (10)
1. A method for identifying dumping behavior of garbage, comprising:
in response to a refuse transfer vehicle being driven into a vehicle weighing position, performing vehicle weighing on the refuse transfer vehicle through a target wagon balance to generate first vehicle weighing information, wherein the target wagon balance is installed at the vehicle weighing position;
reading the vehicle identity information of the garbage transfer vehicle through a vehicle identity card reader;
in response to successful reading, indicating the garbage transfer vehicle to dump garbage;
in response to dumping, performing secondary vehicle weighing on the garbage transfer vehicle through the target wagon balance to generate second vehicle weighing information;
performing dumping behavior recognition on a target video to generate initial dumping behavior recognition information, wherein acquisition time corresponding to a start frame image included in the target video is time for the refuse transfer vehicle to drive into the vehicle weighing position, and acquisition time corresponding to an end frame image included in the target video is time for the refuse transfer vehicle to drive out of a refuse transfer station;
generating dumping behavior identification information according to the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information and the initial dumping behavior identification information, wherein the dumping behavior identification information comprises: dumping quantity of garbage and dumping behavior information.
2. The method of claim 1, wherein prior to the vehicle weighing the refuse transfer vehicle by the target wagon balance in response to the refuse transfer vehicle driving into the vehicle weighing location to generate the first vehicle weighing information, the method further comprises:
in response to determining that the time length of the blocked target infrared light is longer than the preset time length, controlling a target camera to acquire an image to obtain a target image, wherein the target infrared light is an infrared light emitted by an intrusion detector, and the intrusion detector and the target camera are arranged at an inlet of the garbage transfer station;
performing object recognition on the target image to generate recognition object information, wherein the recognition object information comprises: a primary object class and a secondary object class;
in response to determining that the primary object class is a vehicle class and the secondary object class is a refuse transfer vehicle class, generating vehicle guidance information according to a current position of the refuse transfer vehicle and the vehicle weighing position, wherein the vehicle guidance information comprises: guiding a route;
and displaying the vehicle guiding information on a vehicle guiding information display device, wherein the vehicle guiding information display device is arranged in the garbage transfer station and faces to a cockpit of the garbage transfer vehicle so as to be used for guiding the garbage transfer vehicle to drive into the vehicle weighing position.
3. The method of claim 2, wherein the method further comprises:
in response to determining that the dumping behavior information characterizes that the refuse transfer vehicle has not performed a secondary identity reading by the vehicle identity card reader after dumping the refuse, performing the following processing steps:
deleting the vehicle identity information from the target gray list and adding the vehicle identity information to a target black list in response to determining that the vehicle identity information is in the target gray list;
in response to determining that the vehicle identity information is not in the target gray list and is not in the target black list, adding the vehicle identity information to the target gray list;
generating abnormal prompt information of dumping behavior, wherein the abnormal prompt information comprises: abnormal dumping behavior occurrence time, abnormal dumping behavior occurrence place and list category;
and sending the abnormal prompt information to the communication terminal bound with the vehicle identity information.
4. The method of claim 3, wherein the generating toppling behavior identification information from the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial toppling behavior identification information comprises:
Determining a difference between the second vehicle weighing information and the first vehicle weighing information as the garbage dumping amount;
and generating the dumping behavior information according to the vehicle identity information and the initial dumping behavior identification information.
5. The method of claim 4, wherein the performing the pouring behavior recognition on the target video to generate initial pouring behavior recognition information comprises:
performing reverse frame image positioning on the target video to determine a target frame image, wherein the target frame image is an image of the garbage transfer truck after garbage dumping is finished;
the target video is subjected to video segmentation by the target frame image to obtain a first video and a second video, wherein the frame sequence number of an ending frame image included in the first video is smaller than the frame sequence number of a starting frame image included in the second video;
performing similar frame image rejection on the first video to obtain a video after frame image rejection;
and carrying out dumping behavior recognition on the video after the frame images are removed through a pre-trained dumping behavior recognition model included by the cloud server so as to generate initial dumping behavior recognition information.
6. The method of claim 5, wherein after said identifying the pouring behavior of the target video to generate initial pouring behavior identification information, the method further comprises:
determining a garbage residual area through a pre-trained garbage residual area identification model and the second video, wherein the garbage residual area identification model is included by the cloud server;
determining a candidate frame image sequence through the cloud server, wherein the candidate frame images in the candidate frame image sequence are frame images in which the garbage residual area included in the second video is not blocked;
determining a set of residual garbage information through a pre-trained residual garbage identification model and the candidate frame image sequence which are included by the cloud server, wherein the residual garbage information in the set of residual garbage information comprises: residual garbage position information and residual garbage category;
dividing the residual garbage information set according to the residual garbage categories included in the residual garbage information to obtain a first residual garbage information set and a second residual garbage information set, wherein the residual garbage categories included in the first residual garbage information are non-liquid residual garbage categories, and the residual garbage categories included in the second separated residual garbage information are liquid residual garbage categories;
Pushing garbage at a position corresponding to the position information of the residual garbage included in the first residual garbage information into a garbage collection device through a pushing device for each first residual garbage information in the first residual garbage information set;
and for each piece of second residual garbage information in the second residual garbage information set, flushing the corresponding position of the residual garbage position information included in the second residual garbage information through a flushing device.
7. The method of claim 6, wherein the method further comprises:
and controlling a spraying device in the garbage transfer station to spray disinfectant in response to determining that the garbage transfer vehicle drives away from the garbage transfer station.
8. A garbage dumping behavior recognition device, comprising:
a first vehicle weighing unit configured to vehicle weigh a refuse transfer vehicle by a target wagon balance in response to the refuse transfer vehicle being driven into a vehicle weighing location, to generate first vehicle weighing information, wherein the target wagon balance is mounted at the vehicle weighing location;
a reading unit configured to read vehicle identity information of the refuse transfer vehicle through a vehicle identity card reader;
An indicating unit configured to indicate the refuse transfer vehicle to dump refuse in response to a success of reading;
a second vehicle weighing unit configured to perform secondary vehicle weighing on the refuse transfer vehicle through the target wagon balance in response to dumping being completed, to generate second vehicle weighing information;
the dumping behavior recognition unit is configured to recognize dumping behavior of a target video to generate initial dumping behavior recognition information, wherein acquisition time corresponding to a start frame image included in the target video is time when the garbage transfer vehicle is driven into the vehicle weighing position, and acquisition time corresponding to an end frame image included in the target video is time when the garbage transfer vehicle is driven out of a garbage transfer station;
a generation unit configured to generate toppling behavior identification information from the first vehicle weighing information, the vehicle identity information, the second vehicle weighing information, and the initial toppling behavior identification information, wherein the toppling behavior identification information includes: dumping quantity of garbage and dumping behavior information.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 7.
10. A computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1 to 7.
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