CN113436440A - Auxiliary early warning monitoring system for temporary parking - Google Patents
Auxiliary early warning monitoring system for temporary parking Download PDFInfo
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Abstract
The invention relates to a temporary parking auxiliary early warning monitoring system, which comprises: the video acquisition module is used for acquiring real-time videos around the temporary parking spaces through the cameras; the video analysis module comprises a position analysis unit and a license plate recognition unit, the position analysis unit is used for acquiring the position of a vehicle and the position of a parking space according to the real-time video, and the license plate recognition unit is used for acquiring the license plate number information of the vehicle according to the real-time video; the violation judging module is used for judging whether the vehicle parked in the parking space violates the regulations or not through the violation judging step according to the position of the vehicle and the position of the parking space, and if yes, generating an early warning instruction; and the early warning prompting module is used for receiving the early warning instruction and executing the early warning operation. Compared with the prior art, the method has the advantages of good real-time performance, high accuracy, strong objectivity, labor saving, low input cost, high reliability and the like.
Description
Technical Field
The invention relates to a parking space monitoring technology, in particular to an auxiliary early warning monitoring system for temporary parking.
Background
With the continuous increase of the economy of China and the continuous improvement of the living standard of people, the automobile conservation quantity of China shows the trend of increasing year by year. The problems of few parking spaces, difficult parking and the like can be caused, and the problems are more obvious particularly in large cities. Therefore, open parking areas in cities are also produced. However, such an open parking area cannot be installed in a narrow road section, and it is not practical to install a large road area for parking vehicles near a place where people gather, such as a large mall. Therefore, in these areas, the traffic control department usually sets 3-5 temporary parking spaces for the car owner to park for a short time and the passengers to get on or off the car. Therefore, the requirement of the vehicle owner for parking in a short time is met, and the problem of road congestion is effectively relieved. Generally, the owner of the temporary parking space is not allowed to leave the cab, but the owner often ignores the set maximum parking time of the temporary parking space, so that the owner can be identified as a violation by a traffic police department and is penalized.
At present, two management modes, namely manual management and remote monitoring management by using a camera, are usually adopted for temporary parking spaces arranged on roadsides.
Manual management completely relies on a traffic police to manage temporary parking spaces, and a plurality of problems can be faced. Firstly, depending on manual management, the traffic police have the disadvantages of time and labor consumption, long working time and necessity of paying attention to traffic flow at any time; secondly, the parking time is not accurately grasped, the parking time is only roughly estimated, and a reasonable timing scheme does not exist; finally, once the longest parking time is exceeded for the car owner leaving the car during parking, the traffic police cannot quickly find the car owner and drive the car owner away from the parking space.
The camera is used for remote monitoring management, so that a large amount of manual management cost can be saved, and the management pressure of relevant departments is reduced. However, the remote monitoring essentially depends on manual screening by staff according to the video stream, which is easy to cause omission and misjudgment of illegal behaviors. From the perspective of the owner of the vehicle, the remote monitoring and management system achieves the purpose of illegal behavior management, but has no illegal behavior warning function. This allows many owners to be notified by the authorities that their illegal activities have occurred.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide the temporary parking auxiliary early warning monitoring system which has the advantages of good real-time performance, high accuracy, strong objectivity, manpower saving and low input cost; the reliability is high.
The purpose of the invention can be realized by the following technical scheme:
a temporary parking aid pre-warning monitoring system comprising:
the video acquisition module is used for acquiring real-time videos around the temporary parking spaces through the cameras;
the video analysis module comprises a position analysis unit and a license plate recognition unit, wherein the position analysis unit is used for acquiring the position of a vehicle and the position of a parking space according to a real-time video, and the license plate recognition unit is used for acquiring the license plate number information of the vehicle according to the real-time video;
the violation judging module is used for judging whether the vehicle parked in the parking space violates the regulations or not through the violation judging step according to the position of the vehicle and the position of the parking space, and if yes, generating an early warning instruction;
the early warning prompting module is used for receiving an early warning instruction and executing early warning operation;
the monitoring system can automatically monitor whether the vehicle stops and violates regulations, informs a driver of violating regulations in real time, shows that the violating regulations are generated, has good objectivity and saves manpower.
Further, the process of acquiring the vehicle position and the parking space position includes:
and extracting a first detection frame of the vehicle and a second detection frame of the parking space in the real-time video through the trained visual deep learning network.
Further, the violation distinguishing step comprises:
judging whether the overlapping rate k of the first detection frame and the second detection frame is larger than a first threshold value in real time, if so, starting timing, otherwise, stopping operation, wherein the calculation formula of the overlapping rate k is as follows:
wherein S is1Is the area of the second detection frame, S2Is a first detection frame and a second detection frameDetecting the overlapping area of the frames;
setting a second threshold, a third threshold and a fourth threshold in sequence from small to large, and executing early warning prompt operation according to the timing duration, wherein the early warning prompt operation comprises the following steps:
when the timing duration is between a second threshold and a third threshold, generating a primary early warning instruction;
when the timing duration is between a third threshold and a fourth threshold, generating a secondary early warning instruction;
when the timing duration is greater than a fourth threshold value, determining that the vehicle breaks rules and regulations, and generating a three-level early warning instruction;
the early warning prompting module respectively executes the first-stage, second-stage and third-stage early warning operations after receiving the first-stage, second-stage and third-stage early warning instructions;
whether a vehicle is parked in a parking space is judged according to the overlapping rate of the first detection frame and the second detection frame, when a general vehicle enters a roadside parking space, the parking of the vehicle is usually finished in a side direction mode, in the continuous action of parking in the side direction, the overlapping area of the vehicle and the parking space is changed constantly, when the overlapping rate is larger than a first threshold value, the fact that the vehicle enters the parking space is proved, the calculation is simple, the interference caused by environmental factors is small, and the reliability is high;
meanwhile, different early warning levels are set according to the length of time that the vehicle is parked at the parking space, so that the vehicle owner can be reminded of avoiding illegal parking behaviors, corresponding punishment can be made for the vehicle owner who parks for a long time, and the monitoring efficiency is improved.
Furthermore, the early warning prompting module comprises a voice prompting unit, an identity confirming unit, a network prompting unit and a violation archive unit;
the early warning prompting module is used for voice broadcasting prompting information;
the identity confirmation unit inquires the identity information of the vehicle owner according to the license plate number information;
the network prompting unit is used for sending prompting information to a mobile terminal held by the vehicle owner through a communication network according to the identity information of the vehicle owner;
the violation archive unit is used for storing and uploading license plate number information of the violation vehicle;
further, the primary early warning operation includes: and voice broadcasting prompt information.
Further, the secondary early warning operation includes: and voice broadcasting the prompt information and sending the prompt information to the mobile terminal held by the vehicle owner.
Further, the three-stage early warning operation comprises: voice broadcasting prompt information, sending the prompt information to a mobile terminal held by a vehicle owner, and storing and uploading license plate number information of the violation vehicle;
the vehicle driver is reminded through voice broadcast and network communication, the vehicle owner is effectively reminded, and the behavior that the vehicle owner produces or stops illegal parking is avoided.
Furthermore, the visual deep learning network is a yolov4 network, and in the yolov4 network, Soft-NMS is adopted to perform non-extreme value inhibition to obtain a detection frame, so that the accuracy of the detection frame identified under the shielding condition is further improved;
the detection frames of the parking spaces and the vehicles in the video are extracted by utilizing the yolov4 network, so that the false detection rate and the missed detection rate of the vehicles are reduced, meanwhile, a camera does not need to be arranged for each parking space, only one camera needs to be arranged in front of all parking ranges, and the material cost is greatly saved.
Further, a Loss function L for training the visual deep learning network is a replay Loss function, and a calculation formula is as follows:
L＝LAttr+LRepGT+LRepBox
wherein L isAttrLoss values, L, for the predicted frame and the true target frameRepGTLoss values, L, for the prediction box and the adjacent real target boxRepBoxLoss values generated for the prediction box and neighboring prediction boxes that are not predicting the same real target;
in the scene of temporary parking, the false detection and the missed detection of the vehicles are mostly caused by the shielding between the vehicles, namely, the error caused by the shielding in the class is larger, and the shielding between the classesErrors due to gear shift are negligible, LAttrFor the attraction loss function, the distance between the prediction frame and the real frame is reduced by the attraction loss function, LRepGTAnd LRepBoxFor two repulsion loss functions, the repulsion loss functions are utilized to enable the prediction frames to be far away from the real frames of other targets, the maximum distance is kept between the prediction frames of different vehicles, and the false detection rate is reduced;
further, said LAttrThe calculation formula of (2) is as follows:
where P is the set of all prediction boxes, P+For all sets of predicted frames with overlap greater than 0.5 with the true frame, BpIs a specific prediction frame in P,
a real frame with the maximum overlapping degree of a certain prediction frame is taken as the frame;
said LRepGTThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
the real box representing the second largest degree of overlap of a certain prediction box,
is BpAnd
cross-over ratio of (a);
said LRepBoxThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
alpha is the sum of functions of the identity equations and epsilon is a constant, which is the cross-over ratio between the different prediction boxes.
Compared with the prior art, the invention has the following beneficial effects:
(1) the monitoring system comprises a video acquisition module, a video analysis module, a violation judging module and an early warning prompting module, wherein the video acquisition module acquires real-time videos around temporary parking spaces, the video analysis module comprises a position analysis unit and a license plate recognition unit, the position analysis unit is used for acquiring vehicle positions and parking space positions according to the real-time videos, the license plate recognition unit acquires license plate number information of vehicles according to the real-time videos, the violation judging module judges whether the vehicles parked in the parking spaces violate regulations or not according to the vehicle positions and the parking space positions through a violation judging step, and if yes, an early warning instruction is generated; the early warning prompting module receives the early warning instruction and executes the early warning operation, the monitoring system can automatically monitor whether the vehicle stops violating the regulations or not and inform a driver of violating the regulations in real time, the illegal behaviors are shown to be generated, the defects of traditional manual management and remote monitoring management are overcome, the real-time performance is good, the accuracy is high, the objectivity is strong, and the labor is saved;
(2) according to the invention, the trained yolov4 network is used for extracting the first detection frame of the vehicle and the second detection frame of the parking space in the real-time video, so that the false detection rate and the missed detection rate of the vehicle are reduced, meanwhile, a camera does not need to be arranged for each parking space, only one camera is required to be arranged in front of all parking ranges, the problem that temporary parking spaces are difficult to manage can be well solved by using the cameras beside the road, other roadbed equipment does not need to be additionally arranged, a plurality of parking spaces can share one camera, the arrangement is convenient and fast, and the investment cost is low;
(3) the method judges whether the overlapping rate k of the first detection frame and the second detection frame is larger than a first threshold value in real time, if so, timing is started, otherwise, the operation is stopped, when a general vehicle enters a roadside parking space, the vehicle is usually parked in a side direction mode, in the continuous action of parking in the side direction, the overlapping area of the vehicle and the parking space is changed constantly, when the overlapping rate is larger than the first threshold value, the fact that the vehicle enters the parking space is proved, whether the vehicle is parked in the parking space is judged according to the overlapping rate of the first detection frame and the second detection frame, the calculation is simple, the interference of parking environment factors is small, and the reliability is high;
(4) the method comprises the steps that a second threshold, a third threshold and a fourth threshold are set in sequence from small to large, a first-level early warning instruction is generated when the timing duration is between the second threshold and the third threshold, a second-level early warning instruction is generated when the timing duration is between the third threshold and the fourth threshold, the vehicle violation is judged when the timing duration is greater than the fourth threshold, and a third-level early warning instruction is generated;
(5) the invention is considered from the angle of the parking vehicle owner, reminds the vehicle driver through voice broadcasting and network communication modes, effectively reminds the vehicle owner, and avoids the behavior of illegal parking;
(6) according to the method, the Loss function of the yolov4 network is trained to be a replay Loss function, the attraction Loss function and the repulsion Loss function are added into the replay Loss function, the prediction frame is enabled to be closer to the real frame with the maximum corresponding overlapping degree, meanwhile, the prediction frame is enabled to be far away from the real frame with the second largest overlapping degree, the maximum distance is kept among the prediction frames of different targets, and the false detection rate is low.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic view of a temporary parking scene;
FIG. 3 is a schematic view of a portion of a vehicle body driven into a parking space;
fig. 4 is a schematic view of a vehicle body fully driven into a parking space.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
A temporary parking assistance early warning monitoring system, as shown in fig. 1 and 2, comprising:
the video acquisition module 1 is used for acquiring a real-time video around the temporary parking space through a camera;
the video analysis module 2 comprises a position analysis unit 21 and a license plate recognition unit 22, wherein the position analysis unit 21 is used for acquiring a vehicle position and a parking space position according to a real-time video, and the license plate recognition unit 22 is used for acquiring license plate number information of a vehicle according to the real-time video;
the violation judging module 3 is used for judging whether the vehicle parked in the parking space violates the regulations or not through the violation judging step according to the position of the vehicle and the position of the parking space, and if yes, generating an early warning instruction;
the early warning prompting module 4 is used for receiving an early warning instruction and executing early warning operation;
the monitoring system can automatically monitor whether the vehicle stops against the regulations and inform a driver of the regulation against the regulations in real time, so that the condition that the illegal behaviors are generated is indicated, the objectivity is good, and the manpower is saved.
The acquisition process of the vehicle position and the parking space position comprises the following steps:
and extracting a first detection frame of the vehicle and a second detection frame of the parking space in the real-time video through the trained visual deep learning network.
The violation distinguishing step comprises:
and (3) judging whether the overlapping rate is larger than a first threshold value in real time, if so, starting timing, and if not, stopping the operation, wherein the calculation formula of the overlapping rate k is as follows:
wherein S is1Is the area of the second detection frame, S2The overlapping area of the first detection frame and the second detection frame is used as the overlapping area of the first detection frame and the second detection frame;
executing early warning prompt operation according to the timing duration, comprising:
when the timing duration is between 180s and 240s, generating a primary early warning instruction;
when the timing duration is between 240s and 300s, generating a secondary early warning instruction;
when the timing time is longer than 240s, judging that the vehicle breaks rules and regulations, and generating a three-level early warning instruction;
the early warning prompting module 4 respectively executes the first-stage, second-stage and third-stage early warning operations after receiving the first-stage, second-stage and third-stage early warning instructions;
whether the vehicle is parked in the parking space is judged according to the overlapping rate of the first detection frame and the second detection frame, as shown in fig. 3 and 4, when the vehicle generally drives into the roadside parking space, the parking of the vehicle is usually completed in a side direction mode, in the continuous action of parking in the side direction, the overlapping area of the vehicle and the parking space is changed constantly, when the overlapping rate is greater than a first threshold value, the fact that the vehicle drives into the parking space is proved, the calculation is simple, the interference caused by environmental factors is small, and the reliability is high;
meanwhile, different early warning levels are set according to the length of time that the vehicle is parked at the parking space, so that the vehicle owner can be reminded of avoiding illegal parking behaviors, corresponding punishment can be made for the vehicle owner who parks for a long time, and the monitoring efficiency is improved.
The early warning prompting module 4 comprises a voice prompting unit 41, an identity confirming unit 42, a network prompting unit 44 and a violation archive unit 43;
the early warning prompt module 4 is used for voice broadcasting prompt information;
the identity confirmation unit 42 inquires the identity information of the vehicle owner according to the license plate number information;
the network prompting unit 44 is used for sending prompting information to a mobile terminal held by the vehicle owner through a communication network according to the identity information of the vehicle owner;
the violation archive unit 43 is used for storing and uploading license plate number information of the violation vehicle;
the primary early warning operation comprises the following steps: and voice broadcasting prompt information.
The secondary early warning operation comprises the following steps: and voice broadcasting the prompt information and sending the prompt information to the mobile terminal held by the vehicle owner.
The three-stage early warning operation comprises the following steps: voice broadcasting prompt information, sending the prompt information to a mobile terminal held by a vehicle owner, and storing and uploading license plate number information of the violation vehicle;
the vehicle driver is reminded through voice broadcast and network communication, the vehicle owner is effectively reminded, and the behavior that the vehicle owner produces or stops illegal parking is avoided.
The visual depth learning network is a yolov4 network, the yolov4 network utilizes CSPDarknet53 as a main network, feature layers with different depths are obtained by continuously compressing the length and width of a picture and increasing the number of channels, information of the picture is continuously fused by utilizing up-sampling and down-sampling in an SPP structure and a PANet structure to obtain feature graphs with different scales, the obtained features are integrated to obtain a final output result, Soft-NMS is adopted in the yolov4 network to carry out non-extreme value inhibition to obtain a detection frame, and the accuracy of the detection frame identified under the shielding condition is further improved;
the detection frames of the parking spaces and the vehicles in the video are extracted by utilizing the yolov4 network, so that the false detection rate and the missed detection rate of the vehicles are reduced, meanwhile, a camera does not need to be arranged for each parking space, only one camera needs to be arranged in front of all parking ranges, and the material cost is greatly saved.
The Loss function L for training the visual deep learning network is a replay Loss function, and the calculation formula is as follows:
L＝LAttr+LRepGT+LRepBox
wherein L isAttrLoss values, L, for the predicted frame and the true target frameRepGTLoss values, L, for the prediction box and the adjacent real target boxRepBoxLoss values generated for the prediction box and neighboring prediction boxes that are not predicting the same real target;
in the scene of temporary parking, false detection and missed detection of vehicles are mostly caused by shielding between vehicles, namely, errors caused by shielding in classes account for a relatively large proportion, errors caused by shielding between classes can be ignored, and L isAttrFor the attraction loss function, the distance between the prediction frame and the real frame is reduced by the attraction loss function, LRepGTAnd LRepBoxThe method has the advantages that the two repulsion loss functions are utilized, the prediction frames are far away from the real frames of other targets, the maximum distance is kept between the prediction frames of different vehicles, and the false detection rate is reduced.
LAttrThe calculation formula of (2) is as follows:
where P is the set of all prediction boxes, P+For all sets of predicted frames with overlap greater than 0.5 with the true frame, BpIs a specific prediction frame in P,
a real frame with the maximum overlapping degree of a certain prediction frame is taken as the frame;
LRepGTthe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
the real box representing the second largest degree of overlap of a certain prediction box,
is BpAnd
cross-over ratio of (a);
LRepBoxthe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
alpha is the sum of functions of the identity equations and epsilon is a constant, which is the cross-over ratio between the different prediction boxes.
The license plate recognition unit 22 performs coarse positioning, fine positioning and tilt correction processing on the license plate position in the real-time video through the existing visual neural network to obtain a license plate accurate position image, and then performs filtering and binarization processing on the license plate accurate position image to obtain license plate number information.
The embodiment provides an auxiliary early warning and monitoring system for temporary parking, which overcomes the defects of traditional manual management and remote monitoring management, utilizes a deep learning network with excellent real-time performance and accuracy to manage temporary parking spaces, is convenient to deploy and low in investment cost, can well solve the problem that temporary parking spaces are difficult to manage by utilizing a roadside camera, does not need to add other roadbed equipment, can share one camera by a plurality of parking spaces, achieves a good management effect, and greatly saves hardware cost;
moreover, the monitoring system gives consideration to two factors of a management department and a vehicle owner, from the perspective of the management department, a large amount of manpower and financial resources can be saved, the monitoring system plays a role in warning the impending illegal parking behavior of the vehicle, correspondingly punishment is carried out on the illegal parking behavior which occurs, the monitoring system records the illegal parking behavior, and from the perspective of the vehicle owner, the traditional remote monitoring management system proves that the illegal behavior is generated when the vehicle owner receives the notice of the management department. The invention adds the early warning function, executes the early warning operation of three levels according to the parking time, gives the car owner sufficient time to drive the car away from the parking space, thereby avoiding the illegal action.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. The utility model provides a supplementary early warning monitored control system of temporary parking which characterized in that includes:
the video acquisition module is used for acquiring real-time videos around the temporary parking spaces through the cameras;
the video analysis module comprises a position analysis unit and a license plate recognition unit, wherein the position analysis unit is used for acquiring the position of a vehicle and the position of a parking space according to a real-time video, and the license plate recognition unit is used for acquiring the license plate number information of the vehicle according to the real-time video;
the violation judging module is used for judging whether the vehicle parked in the parking space violates the regulations or not through the violation judging step according to the position of the vehicle and the position of the parking space, and if yes, generating an early warning instruction;
and the early warning prompting module is used for receiving the early warning instruction and executing the early warning operation.
2. A temporary parking aid early warning monitoring system as claimed in claim 1, wherein the vehicle location and parking space location obtaining process comprises:
and extracting a first detection frame of the vehicle and a second detection frame of the parking space in the real-time video through the trained visual deep learning network.
3. The temporary parking assistant pre-warning monitoring system as claimed in claim 2, wherein the violation distinguishing step comprises:
judging whether the overlapping rate k of the first detection frame and the second detection frame is larger than a first threshold value in real time, if so, starting timing, otherwise, stopping operation, wherein the calculation formula of the overlapping rate k is as follows:
wherein S is1Is the area of the second detection frame, S2The overlapping area of the first detection frame and the second detection frame is used as the overlapping area of the first detection frame and the second detection frame;
setting a second threshold, a third threshold and a fourth threshold in sequence from small to large, and executing early warning prompt operation according to the timing duration, wherein the early warning prompt operation comprises the following steps:
when the timing duration is between a second threshold and a third threshold, generating a primary early warning instruction;
when the timing duration is between a third threshold and a fourth threshold, generating a secondary early warning instruction;
when the timing duration is greater than a fourth threshold value, determining that the vehicle breaks rules and regulations, and generating a three-level early warning instruction;
and the early warning prompting module respectively executes the first-stage, second-stage and third-stage early warning operations after receiving the first-stage, second-stage and third-stage early warning instructions.
4. The temporary parking auxiliary early warning monitoring system as claimed in claim 3, wherein the early warning prompting module comprises a voice prompting unit, an identity confirming unit, a network prompting unit and a violation archive unit;
the early warning prompting module is used for voice broadcasting prompting information;
the identity confirmation unit inquires the identity information of the vehicle owner according to the license plate number information;
the network prompting unit is used for sending prompting information to a mobile terminal held by the vehicle owner through a communication network according to the identity information of the vehicle owner;
and the violation archive unit is used for storing and uploading license plate number information of the violation vehicle.
5. The temporary parking assistance pre-warning monitoring system as claimed in claim 4, wherein the primary pre-warning operation comprises: and voice broadcasting prompt information.
6. A temporary parking aid pre-warning monitoring system as recited in claim 4, wherein said secondary warning operations comprise: and voice broadcasting the prompt information and sending the prompt information to the mobile terminal held by the vehicle owner.
7. A temporary parking aid pre-warning monitoring system as recited in claim 4, wherein said three-level warning operation comprises: and voice broadcasting prompt information, sending the prompt information to a mobile terminal held by the owner, and storing and uploading license plate number information of the violation vehicle.
8. A temporary parking aid pre-warning monitoring system as claimed in claim 2, wherein the visual deep learning network is yolov4 network.
9. The temporary parking auxiliary early warning monitoring system according to claim 8, wherein the Loss function L for training the visual deep learning network is a distribution Loss function, and the calculation formula is as follows:
L＝LAttr+LRepGT+LRepBox
wherein L isAttrLoss values, L, for the predicted frame and the true target frameRepGTLoss values, L, for the prediction box and the adjacent real target boxRepBoxLoss values that are generated for a prediction box and a neighboring prediction box that is not predicting the same real target.
10. A temporary parking aid pre-warning monitoring system as claimed in claim 5, wherein LAttrThe calculation formula of (2) is as follows:
wherein P is all prediction blocksSet, P+For all sets of predicted frames with overlap greater than 0.5 with the true frame, BpIs a specific prediction frame in P,
a real frame with the maximum overlapping degree of a certain prediction frame is taken as the frame;
said LRepGTThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
the real box representing the second largest degree of overlap of a certain prediction box,
is BpAnd
cross-over ratio of (a);
said LRepBoxThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
alpha is the sum of functions of the identity equations and epsilon is a constant, which is the cross-over ratio between the different prediction boxes.
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