CN112163544A - Method and system for judging random placement of non-motor vehicles - Google Patents
Method and system for judging random placement of non-motor vehicles Download PDFInfo
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Abstract
The invention discloses a method and a system for judging the random placement of non-motor vehicles, wherein the method comprises the following steps: the method comprises the steps of obtaining a video stream of a scene to be detected shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images; carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected; extracting a minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing; classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement; and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle. The method provided by the invention improves the accuracy of judging the random placement of the non-motor vehicles, saves resources and is flexible and easy to use.
Description
Technical Field
The invention relates to the technical field of classification, in particular to a method and a system for judging the random placement of non-motor vehicles.
Background
In recent years, with the acceleration of urbanization progress, the improvement of living standard of people, the development of shared economy and the advocation of green travel in more and more cities, bicycle travel is greatly promoted, and a non-motor vehicle lane special for parking a non-motor vehicle is planned and marked with a striking lane line. At present, shared bicycles are distributed in various big cities and become indispensable travel tools in life of people, the workload of a city management office is increased due to the disordered placement of bicycles, at present, a video identification technical scheme is adopted, vehicle information cannot be effectively identified for non-motor vehicles, although the technical scheme of adopting license plate video identification and face identification is adopted, the license plate identification difficulty is higher due to the fact that the non-motor vehicles do not have license plates with unified specifications and the installation positions and the installation modes of the license plates are not unified; on the other hand, the non-motor vehicle type is not unified, the recognition rate is low, the efficiency of non-motor vehicle video recognition is influenced, and the problem of low recognition rate of the phenomenon that non-motor vehicles are randomly placed exists.
Disclosure of Invention
Therefore, the method and the system for judging the random placement of the non-motor vehicles overcome the defect of low recognition rate of the random placement of the non-motor vehicles in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for determining a random arrangement of a non-motor vehicle, including:
the method comprises the steps of obtaining a video stream of a scene to be detected shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images;
carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected;
extracting a minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing;
classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement;
and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle.
In one embodiment, the placing label includes: the arrangement is orderly, the arrangement is not orderly and no non-motor vehicles are arranged.
In one embodiment, the training process for the non-motor vehicle random placement classification model comprises the following steps:
acquiring an image of an actual traffic scene, performing data enhancement on the image of the actual traffic scene, performing line drawing configuration on a frame drawing image subjected to data enhancement, labeling a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area of the traffic scene, and synthesizing a training data set;
extracting a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area in a training data set, inputting the minimum external rectangular frame into a network model as a regression parameter true value, calculating a regression loss function, and training the network model;
inputting the information of the minimum external rectangular frame and the vehicle label into a network model, calculating a loss value, adjusting the learning rate, performing cyclic training, stopping training until the loss value is smaller than a first preset threshold value or the number of cyclic times is larger than a preset value, and obtaining a trained non-motor vehicle random placement classification model.
In one embodiment, the FFicientNet B3 model is adopted to carry out classification training on the random placement of the non-motor vehicles.
In one embodiment, the method further comprises: and when the placement is irregular and the confidence coefficient is greater than a second preset threshold value, outputting early warning information of the irregular placement.
In an embodiment, the warning information further includes: the place and time of the non-motor vehicles in random arrangement.
In a second aspect, an embodiment of the present invention provides a system for determining a random arrangement of a non-motor vehicle, including:
the image acquisition module is used for acquiring a video stream of a scene to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images;
the marking configuration module is used for carrying out marking configuration of a preset detection area on the frame-drawing image in the scene video stream to be detected;
the image processing module is used for extracting the minimum external rectangular frame configured by frame-drawing image lineation and performing mask processing to obtain a local image after the mask processing;
the model classification module is used for classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement;
and the model judging module is used for judging whether the non-motor vehicle to be detected has the random placement phenomenon according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle.
In a third aspect, an embodiment of the present invention provides a terminal, including: the present invention relates to a method for determining the presence of a non-motor vehicle in a vehicle, and more particularly, to a method for determining the presence of a non-motor vehicle in a vehicle, which includes a first step of determining the presence of a non-motor vehicle in a vehicle, a second step of determining the presence of a non-motor vehicle in a vehicle, and a third step of determining the presence of a non-motor vehicle in a vehicle.
In a fourth aspect, the embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the method for determining the random arrangement of the non-motor vehicle according to the first aspect of the embodiment of the present invention.
The technical scheme of the invention has the following advantages:
the invention provides a method and a system for judging the random arrangement of non-motor vehicles, which are characterized in that a video stream of a scene to be detected, which is shot by image acquisition equipment in real time, is obtained, wherein the video stream comprises the following steps: a plurality of frames of images; carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected; extracting a minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing; classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement; and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle. According to the method, through training of a non-motor vehicle random placement classification model, scribing configuration of a preset detection area is carried out according to frame extraction images in different scenes to be detected, a minimum external rectangular frame of the frame extraction image scribing configuration is extracted and subjected to mask processing, a local image after the mask processing is obtained, the local image is input into the non-motor vehicle random placement classification model, and whether the non-motor vehicle to be detected has a random placement phenomenon is judged according to a placement label and confidence coefficient of the random placement of the non-motor vehicle. The method provided by the invention improves the accuracy of judging the random placement of the non-motor vehicles, saves resources and is flexible and easy to use.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a method for determining the random placement of a non-motor vehicle according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system for determining the random placement of a non-motor vehicle according to an embodiment of the present invention;
fig. 3 is a composition diagram of a specific example of a terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The method for judging the random placement of the non-motor vehicles, which is provided by the embodiment of the invention, as shown in fig. 1, comprises the following steps:
step S1: the method comprises the steps of obtaining a video stream of a scene to be detected shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images.
In the embodiment of the invention, the corresponding camera shooting acquisition device is installed to acquire the video stream of the scene to be detected in real time, the video stream is composed of a plurality of frames of images, and the corresponding camera shooting acquisition device is selected according to actual requirements in practical application by taking the example as an example and not limiting the example; according to the requirement of the scene detection to be detected, the installation positions and the number of the corresponding camera shooting acquisition devices are selected, and corresponding change is carried out according to actual requirements in practice.
Step S2: and carrying out marking configuration of a preset detection area on the frame extraction image in the scene video stream to be detected.
In the embodiment of the present invention, the frame extraction image is a key frame in the extracted video stream, where the key frame is: an image is extracted every twenty minutes, which is only taken as an example and not limited to this, and in practical application, corresponding time intervals are selected according to practical requirements for frame extraction; the preset detection area is a scene to be detected of the non-motor vehicle, and the corresponding scene to be detected is selected according to actual requirements in practical application.
In the embodiment of the present invention, the method for performing scribing configuration according to different scenes to be detected includes: drawing a polygon of the area to be detected, and marking the polygon by adopting manual marking; for example: the bicycle area is marked along the edge of a row of bicycles, the areas at two ends are properly prolonged, when the shape of the mark frame mark is quadrilateral, the mark starts from the upper left corner, the information of the vertex of the polygon is recorded simultaneously according to the clockwise direction, and the corresponding polygon and the marking method are selected for marking according to the actual requirement of the area to be detected in the practical application. Compared with the algorithm, the method adopting the scribing configuration saves the video memory resource and saves the time consumed by the algorithm.
Step S3: and extracting the minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing.
In the embodiment of the invention, the local picture of the non-motor vehicle is extracted, namely the minimum circumscribed rectangular frame of the scribing configuration picture is extracted, and the mask processing is carried out on the outside of the region of the minimum circumscribed rectangular frame of the extracted local picture.
Transmitting the processed pictures to a task end in a json data format, and sending the pictures to a non-motor vehicle random placement classification model pre-trained in the step S4, wherein input data mainly comprise: information such as an ID of a video capture device, an ID of a current frame, a type of input data, a data format, image data, a size of an image, a frame frequency of a video stream, an ID of a server, a task ID, a current time, and a video ID: by way of example only, and not by way of limitation, the corresponding input data is selected in practical applications.
Step S4: and classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles random placement.
In the embodiment of the invention, the training process of the non-motor vehicle random placement classification model comprises the following steps: acquiring an image of an actual traffic scene, performing data enhancement on the image of the actual traffic scene, performing line drawing configuration on a frame drawing image subjected to data enhancement, labeling a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area of the traffic scene, and synthesizing a training data set; extracting a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area in a training data set, inputting the minimum external rectangular frame into a network model as a regression parameter true value, calculating a regression loss function, and training the network model; inputting the information of the minimum circumscribed rectangle frame and the vehicle label into a network model, calculating a loss value, adjusting the learning rate, and performing cyclic training until the loss value is smaller than a first preset threshold or the number of cycles is larger than a preset value (the first preset threshold and the preset value can be set according to an expert experience value without limitation), stopping training, and obtaining a trained non-motor vehicle random placement classification model.
In the training process of the non-motor vehicle random placement classification model, acquiring practical application scenes, such as: the image data of non-motor vehicles under the city management monitoring camera can select about 300 video point locations of a plurality of cities, from six points early to eight points late, the video with the recording duration of 1 minute at intervals of twenty minutes, the recording time is about one week in total, representative pictures are extracted from about 10 ten thousand videos obtained, multi-angle samples are collected from a plurality of cities, a plurality of scenes and a plurality of time periods, each picture requires that the pictures are clear, human eyes are easy to distinguish, and when the pictures are collected actually, corresponding representative pictures are selected according to actual requirements. And simultaneously, performing data enhancement on the selected picture, wherein the data enhancement method comprises the following steps: the data set is further expanded by methods of random rotation, random blurring, random color transformation, normalization and the like, which are only used as examples, but not limited thereto, and a corresponding data enhancement mode is selected according to actual requirements in actual application. Carrying out marking configuration on the data enhanced frame-drawing image, marking a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area of a traffic scene, and synthesizing a training data set;
in an embodiment of the present invention, the placing of the label includes: the arrangement is orderly, the arrangement is not orderly and no non-motor vehicles are arranged. For example: the bicycle placement area in the scene is far, and no marking is carried out in the situation; neatness and irregularity are distinguished: the conditions that the placement of the bicycles is disordered and the front and the back of the bicycles are seriously uneven are considered as irregular, and the classification standard of the corresponding placement label is selected according to the actual requirement in the practical application by taking the example as an example and not by taking the example as a limitation.
In the embodiment of the invention, based on a deep learning theory, a swish activation function of an EfficientNet B3 model is adopted to carry out classification training on the random placement of the non-motor vehicles. Extracting a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area in a training data set, inputting the minimum external rectangular frame into a network model as a regression parameter true value, calculating a regression loss function, and training the network model; inputting the information of the minimum circumscribed rectangle frame and the vehicle label into a network model, calculating a loss value, adjusting the learning rate, and performing cyclic training until the loss value is smaller than a first preset threshold or the number of cycles is larger than a preset value (the first preset threshold and the preset value can be set according to an expert experience value without limitation), stopping training, and obtaining a trained non-motor vehicle random placement classification model.
Step S5: and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle.
In the embodiment of the invention, when the placed labels are placed irregularly and the confidence coefficient is greater than a second preset threshold value, early warning information of the placed irregularly is output, and a corresponding threshold value is selected according to actual requirements in actual application; the early warning information includes: the place and time of the random placement of the non-motor vehicle are only given as examples and are not limited to the place and time.
The method for judging the random arrangement of the non-motor vehicles, provided by the embodiment of the invention, comprises the following steps of obtaining a video stream of a scene to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images; carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected; extracting a minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing; classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement; and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle. According to the embodiment of the invention, through training of the classification model of the random arrangement of the non-motor vehicles, the marking configuration of the preset detection area is carried out according to the frame-drawing images in different scenes to be detected, the minimum external rectangular frame of the marking configuration of the frame-drawing images is extracted and subjected to mask processing to obtain the local image after the mask processing, the local image is input into the classification model of the random arrangement of the non-motor vehicles, and whether the random arrangement phenomenon exists in the non-motor vehicles to be detected is judged according to the arrangement label and the confidence coefficient of the random arrangement of the non-motor vehicles. The method provided by the embodiment of the invention improves the accuracy of judging the random placement of the non-motor vehicles, saves resources and is flexible and easy to use.
Example 2
An embodiment of the present invention provides a system for determining a random arrangement of a non-motor vehicle, as shown in fig. 2, including:
the image acquisition module 1 is configured to acquire a video stream of a scene to be detected, which is captured by an image acquisition device in real time, where the video stream includes: a plurality of frames of images; this module executes the method described in step S1 in embodiment 1, and is not described herein again.
The marking configuration module 2 is used for marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected; this module executes the method described in step S2 in embodiment 1, and is not described herein again.
The image processing module 3 is used for extracting a minimum external rectangular frame configured by frame-drawing image lineation and performing mask processing to obtain a local image after mask processing; this module executes the method described in step S3 in embodiment 1, and is not described herein again.
The model classification module 4 is used for classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement; this module executes the method described in step S4 in embodiment 1, and is not described herein again.
The model judging module 5 is used for judging whether the non-motor vehicle to be detected has the random placement phenomenon according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle; this module executes the method described in step S5 in embodiment 1, and is not described herein again.
The embodiment of the invention provides a system for judging the random arrangement of non-motor vehicles, which obtains a video stream of a scene to be detected, which is shot by an image acquisition device in real time, through an image acquisition module, wherein the video stream comprises the following components: a plurality of frames of images; the marking configuration module is used for carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected; in an image processing module, extracting a minimum external rectangular frame configured by frame-drawing image lineation and performing mask processing to obtain a local image after mask processing; in a model classification module, classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles random placement; and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle. The accuracy of judging the random placement of the non-motor vehicles is improved, and meanwhile, the resource is saved, and the device is flexible and easy to use. According to the embodiment of the invention, through training of the classification model of the random arrangement of the non-motor vehicles, the marking configuration of the preset detection area is carried out according to the frame-drawing images in different scenes to be detected, the minimum external rectangular frame of the marking configuration of the frame-drawing images is extracted and subjected to mask processing to obtain the local image after the mask processing, the local image is input into the classification model of the random arrangement of the non-motor vehicles, and whether the random arrangement phenomenon exists in the non-motor vehicles to be detected is judged according to the arrangement label and the confidence coefficient of the random arrangement of the non-motor vehicles. The method provided by the embodiment of the invention improves the accuracy of judging the random placement of the non-motor vehicles, saves resources and is flexible and easy to use.
Example 3
An embodiment of the present invention provides a terminal, as shown in fig. 3, including: at least one processor 401, such as a CPU (Central Processing Unit), at least one communication interface 403, memory 404, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The communication interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a standard wireless interface. The Memory 404 may be a high-speed RAM Memory (Random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 404 may optionally be at least one memory device located remotely from the processor 401. Wherein the processor 401 may execute the method for determining the random arrangement of the non-motor vehicles in embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the method for judging the non-motor vehicle misplacement in embodiment 1. The communication bus 402 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in FIG. 3, but this does not represent only one bus or one type of bus. The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above. The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above.
The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 401 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 404 is also used to store program instructions. Processor 401 may call program instructions to implement the method for determining the random placement of a non-motor vehicle according to embodiment 1.
The embodiment of the invention further provides a computer-readable storage medium, wherein computer-executable instructions are stored on the computer-readable storage medium, and the computer-executable instructions can execute the method for judging the random arrangement of the non-motor vehicles in the embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (9)
1. A method for judging the random arrangement of non-motor vehicles is characterized by comprising the following steps:
the method comprises the steps of obtaining a video stream of a scene to be detected shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images;
carrying out marking configuration of a preset detection area on a frame extraction image in a scene video stream to be detected;
extracting a minimum external rectangular frame configured by drawing lines of the frame-drawing image and performing mask processing to obtain a local image after the mask processing;
classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement;
and judging whether the non-motor vehicle to be detected has the random placement phenomenon or not according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle.
2. The method for determining whether a non-motor vehicle is in a disorderly state according to claim 1, wherein the placing label comprises: the arrangement is orderly, the arrangement is not orderly and no non-motor vehicles are arranged.
3. The method for determining the random placement of a non-motor vehicle according to claim 1, wherein the training process of the classification model of the random placement of the non-motor vehicle comprises:
acquiring an image of an actual traffic scene, performing data enhancement on the image of the actual traffic scene, performing line drawing configuration on a frame drawing image subjected to data enhancement, labeling a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area of the traffic scene, and synthesizing a training data set;
extracting a minimum external rectangular frame and a vehicle label of a non-motor vehicle target detection area in a training data set, inputting the minimum external rectangular frame into a network model as a regression parameter true value, calculating a regression loss function, and training the network model;
inputting the information of the minimum external rectangular frame and the vehicle label into a network model, calculating a loss value, adjusting the learning rate, performing cyclic training, stopping training until the loss value is smaller than a first preset threshold value or the number of cyclic times is larger than a preset value, and obtaining a trained non-motor vehicle random placement classification model.
4. The method for determining the random placement of non-motor vehicles according to claim 3, wherein the classification training of the random placement of non-motor vehicles is performed by using an EfficientNet B3 model.
5. The method for determining whether a non-motor vehicle is in a disorderly state according to claim 2, further comprising: and when the placement is irregular and the confidence coefficient is greater than a second preset threshold value, outputting early warning information of the irregular placement.
6. The method according to claim 5, wherein the warning information further comprises: the place and time of the non-motor vehicles in random arrangement.
7. A system for determining whether a non-motor vehicle is in a disorderly state, comprising:
the image acquisition module is used for acquiring a video stream of a scene to be detected, which is shot by image acquisition equipment in real time, wherein the video stream comprises: a plurality of frames of images;
the marking configuration module is used for carrying out marking configuration of a preset detection area on the frame-drawing image in the scene video stream to be detected;
the image processing module is used for extracting the minimum external rectangular frame configured by frame-drawing image lineation and performing mask processing to obtain a local image after the mask processing;
the model classification module is used for classifying the local images after mask processing by utilizing a pre-trained non-motor vehicle random placement classification model to generate placement labels and confidence coefficients of the non-motor vehicles in random placement;
and the model judging module is used for judging whether the non-motor vehicle to be detected has the random placement phenomenon according to the placement label and the confidence coefficient of the random placement of the non-motor vehicle.
8. A terminal, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the at least one processor to perform the method of determining the disarrangement of a non-motor vehicle of any of claims 1-6.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of determining whether a non-motor vehicle is in a random position according to any one of claims 1 to 6.
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