CN114973694A - Tunnel traffic flow monitoring system and method based on inspection robot - Google Patents
Tunnel traffic flow monitoring system and method based on inspection robot Download PDFInfo
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Abstract
The invention belongs to the technical field of inspection robots, and discloses a tunnel traffic flow monitoring system based on an inspection robot and a method thereof, wherein the method comprises the following steps: establishing a vehicle target identification model and a routing inspection track fault detection model; carrying out fault detection on the inspection track according to the inspection track video data by using an inspection track fault detection model; acquiring real-time vehicle passing video data and real-time polling track video data in a tunnel; preprocessing the real-time vehicle passing video data to obtain preprocessed vehicle passing image data of continuous frames; sequentially inputting the preprocessed vehicle passing image data of the continuous frames into a vehicle target recognition model for vehicle target recognition and tracking; and calculating the traffic flow according to the target number of the vehicles in the sampling period. The invention solves the problems of difficult monitoring of vehicle flow in the tunnel, low data transmission efficiency, low safety, high labor cost input and low accuracy in the prior art.
Description
Technical Field
The invention belongs to the technical field of inspection robots, and particularly relates to a tunnel traffic flow monitoring system and method based on an inspection robot.
Background
Along with the development of traffic construction, a large number of mountaineering or sea-crossing tunnels are arranged, so that the living quality and the traveling quality of citizens are improved, the difficulty in monitoring and managing the vehicle flow in the tunnels is increased, the data transmission efficiency is low due to narrow space and poor signals in the tunnels, a manual monitoring mode is usually adopted, the safety is low, the labor cost investment is high, the vehicle passing speed is high, the mode of field statistics or video statistics is only carried out by naked eyes, and the accuracy is low.
Disclosure of Invention
The invention aims to solve the problems of difficulty in monitoring vehicle flow in a tunnel, low data transmission efficiency, low safety, high labor cost input and low accuracy in the prior art, and provides a tunnel vehicle flow monitoring system and a tunnel vehicle flow monitoring method based on an inspection robot.
The technical scheme adopted by the invention is as follows:
the utility model provides a tunnel traffic flow monitoring system based on robot patrols and examines, includes patrols and examines the robot, patrols and examines the track, edge calculation gateway and surveillance center, patrols and examines the robot and sets up in patrolling and examining the track top, patrols and examines the robot and be provided with vehicle video acquisition unit towards the current one side of vehicle, and patrols and examines robot and edge calculation gateway communication connection, patrols and examines the track and sets up in inside one side of tunnel, and edge calculation gateway sets up in the inside top of tunnel, and edge calculation gateway and surveillance center communication connection.
Furthermore, the inspection robot comprises a body, a mobile unit arranged at the bottom end of the body, a vehicle video acquisition unit arranged on one side of the body facing the vehicle to pass through, a track video acquisition unit arranged at one end of the body, and a robot main control unit and a rechargeable battery arranged inside the body, wherein the robot main control unit is electrically connected with the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively, the robot main control unit is in communication connection with the edge computing gateway, the mobile unit is arranged at the top end of the inspection track, and the rechargeable battery is electrically connected with the robot main control unit, the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively;
the robot main control unit comprises a robot main control module, a positioning module, a first storage module, a motor driving module and a wireless communication module, wherein the robot main control module is respectively and electrically connected with the positioning module, the first storage module, the motor driving module and the wireless communication module;
the vehicle video acquisition unit is first high-speed infrared camera, and first high-speed infrared camera sets up towards the current one side of vehicle, and track video acquisition unit is the high-speed infrared camera of second that sets up at the body both ends, and the track setting is patrolled and examined to the high-speed infrared camera orientation of second.
Furthermore, the edge computing gateway comprises an edge computing unit and a network unit, the edge computing unit is electrically connected with the network unit, and the network unit is respectively in communication connection with the wireless communication module of the inspection robot and the monitoring center;
the edge calculation unit comprises an edge calculation main control module, a second storage module, an image preprocessing module and an encryption module, wherein the edge calculation main control module is respectively connected with the second storage module, the image preprocessing module, the encryption module and the network unit.
Furthermore, the monitoring center is provided with a data server, the data server is in communication connection with the edge computing gateway, and the data server is in communication connection with an external cloud data center;
the data server comprises a data parallel receiving module, a decryption module, a vehicle target identification module, a traffic flow calculation module, an inspection track fault detection module, a cache database module and a data parallel uploading module, wherein the data parallel receiving module is respectively connected with the edge calculation gateway and the decryption module;
the vehicle target identification module is provided with a vehicle target identification model, and the inspection track fault detection module is provided with an inspection track fault detection model.
A tunnel traffic flow monitoring method based on an inspection robot is based on a tunnel traffic flow monitoring system and comprises the following steps:
establishing a vehicle target identification model and a routing inspection track fault detection model;
carrying out fault detection on the inspection track according to the last inspection track video data by using an inspection track fault detection model, if a serious fault exists, sending an alarm signal, and stopping a tunnel vehicle flow monitoring method, otherwise, entering the next step;
acquiring real-time vehicle passing video data and real-time polling track video data in a tunnel;
preprocessing the real-time vehicle passing video data to obtain preprocessed vehicle passing image data of continuous frames;
sequentially inputting the preprocessed vehicle passing image data of the continuous frames into a vehicle target recognition model for vehicle target recognition and tracking to obtain the number of vehicle targets;
and calculating the traffic flow according to the target number of the vehicles in the sampling period to obtain a tunnel traffic flow result.
Further, the method for establishing the vehicle target identification model and the inspection track fault detection model comprises the following steps:
acquiring a historical vehicle passing image data set and a historical inspection track image data set, and performing data set expansion and pretreatment on the historical vehicle passing image data set and the historical inspection track image data set to obtain an expanded vehicle passing training data set and an expanded inspection track training data set;
establishing an initial vehicle target identification model based on a YOLOv5 algorithm, and establishing an initial inspection rail fault detection model based on a PP-YOLO-Tiny algorithm;
inputting the vehicle passing training data set into an initial vehicle target recognition model for optimization training to obtain an optimal vehicle target recognition model;
and inputting the polling track training data set into an initial polling track fault detection model to obtain an optimal polling track fault detection model.
Further, the network structure of the vehicle target identification model comprises a first input end, a backhaul module, a first detectionNeck module and a first Prediction module which are sequentially connected;
the network structure of the inspection track fault detection model comprises a second input end, a MobileNetV3 module, a second detective Neck module, a detective head module and a second Prediction module which are sequentially connected.
Further, the method for detecting the faults of the patrol track by using the patrol track fault detection model according to the last patrol track video data comprises the following steps:
frame interception and pretreatment are carried out on the last polling track video data to obtain the polling track video data after pretreatment of continuous frames;
sequentially inputting the preprocessed inspection track video data of the continuous frames into an inspection track fault detection model for target detection to obtain a plurality of inspection track fault target images;
carrying out fault detection on the plurality of routing inspection track fault target images to obtain a fault detection result;
the failure detection results include severe failure, moderate damage, and mild bruising.
Further, the method for preprocessing the real-time vehicle passing video data comprises the following steps:
frame interception is carried out on the real-time vehicle passing video data to obtain initial vehicle passing image data of continuous frames;
and carrying out denoising processing, gray level processing and normalization processing on the initial vehicle passing image data of the continuous frames to obtain the preprocessed vehicle passing image data of the continuous frames.
Further, the vehicle passing image data after the preprocessing of the continuous frames is sequentially input into a vehicle target recognition model for vehicle target recognition and tracking, and the method comprises the following steps:
carrying out grid division on the preprocessed vehicle passing image data of all frames, and acquiring a priori frame of each grid;
acquiring an initial prediction frame corresponding to each prior frame according to the offset of the prior frame and a preset prediction frame;
carrying out non-maximum suppression screening on the initial prediction frame according to a preset intersection ratio and a preset confidence coefficient to obtain a final prediction frame of the preprocessed vehicle passing image data of all frames;
carrying out vehicle target identification on the image area in the final prediction frame to obtain a plurality of vehicle targets;
and tracking and counting the same vehicle target by using a multi-target tracking algorithm to obtain the number of the vehicle targets.
The invention has the beneficial effects that:
1) according to the tunnel traffic flow monitoring system based on the inspection robot, the inspection robot and the inspection track are adopted to monitor traffic flow in a tunnel, a manual mode is avoided, safety and accuracy are improved, tunnel traffic flow monitoring difficulty is reduced, labor cost input is reduced, meanwhile, data transmission can be achieved under the condition that no network exists by adopting the edge computing gateway, and data transmission efficiency is improved.
2) According to the tunnel traffic flow monitoring method based on the inspection robot, the vehicle target and the inspection track fault target are automatically detected and identified based on the deep learning algorithm, manual identification and statistics are avoided, the monitoring accuracy is improved, inspection track fault detection is performed between each tunnel traffic flow monitoring, the normal work of the inspection robot is guaranteed, and the practicability and the safety are improved.
Other advantageous effects of the present invention will be further described in the detailed description.
Drawings
Fig. 1 is a structural block diagram of a tunnel traffic flow monitoring system based on an inspection robot in the invention.
Fig. 2 is a flow chart of the tunnel traffic flow monitoring method based on the inspection robot in the invention.
Detailed Description
The invention is further explained by the following embodiments in combination with the drawings.
Example 1:
as shown in fig. 1, this embodiment provides a tunnel traffic flow monitoring system based on robot patrols and examines, including setting up the robot patrols and examines in different tunnels, patrol and examine the track, edge calculation gateway and only surveillance center, patrol and examine the robot and set up in patrolling and examining the track top, patrol and examine the robot and be provided with vehicle video acquisition unit towards the current one side of vehicle, and patrol and examine robot and edge calculation gateway communication connection, patrol and examine the track and set up in the inside one side in tunnel, edge calculation gateway sets up in the inside top in tunnel, and edge calculation gateway and surveillance center communication connection.
The inspection track guarantees the normal action of the inspection robot, the vehicle video acquisition unit is used for acquiring vehicle passing video data in a tunnel, the edge computing gateway is used for data transmission, the monitoring center monitors the traffic flow according to the vehicle passing video data, the edge computing gateway can guarantee data transmission under the condition of network difference or network loss in the tunnel, and meanwhile the edge computing gateway has certain edge computing capability.
Preferably, the inspection robot comprises a body, a mobile unit arranged at the bottom end of the body, a vehicle video acquisition unit arranged on one side of the body facing the vehicle to pass through, a track video acquisition unit arranged at one end of the body, and a robot main control unit and a rechargeable battery arranged inside the body, wherein the robot main control unit is electrically connected with the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively, the robot main control unit is in communication connection with the edge computing gateway, the mobile unit is arranged at the top end of the inspection track, and the rechargeable battery is electrically connected with the robot main control unit, the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively;
the robot main control unit comprises a robot main control module, a positioning module, a first storage module, a motor driving module and a wireless communication module, wherein the robot main control module is electrically connected with the positioning module, the first storage module, the motor driving module and the wireless communication module respectively;
the vehicle video acquisition unit is first high-speed infrared camera, and first high-speed infrared camera sets up towards the current one side of vehicle, and track video acquisition unit is the high-speed infrared camera of second that sets up at the body both ends, and the track setting is patrolled and examined to the high-speed infrared camera orientation of second.
Preferably, the edge computing gateway comprises an edge computing unit and a network unit, the edge computing unit is electrically connected with the network unit, and the network unit is respectively in communication connection with the wireless communication module of the inspection robot and the monitoring center;
the edge calculation unit comprises an edge calculation main control module, a second storage module, an image preprocessing module and an encryption module, wherein the edge calculation main control module is respectively connected with the second storage module, the image preprocessing module, the encryption module and the network unit; the video data preprocessing is placed in the edge computing unit, the data processing pressure of the monitoring center can be reduced, the storage space of the second storage module can be saved, the monitoring center broadcasts public keys to all the edge computing units, corresponding private keys are locally kept, the encryption module encrypts and uploads vehicle passing image data and the like collected by the inspection robot according to the public keys, the encrypted data are decrypted by the private keys in the monitoring center, and the safety of data transmission is guaranteed.
Preferably, the monitoring center is provided with a data server, the data server is in communication connection with the edge computing gateway, and the data server is in communication connection with an external cloud data center;
the data server comprises a data parallel receiving module, a decryption module, a vehicle target identification module, a traffic flow calculation module, an inspection track fault detection module, a cache database module and a data parallel uploading module, wherein the data parallel receiving module is respectively connected with the edge calculation gateway and the decryption module;
the vehicle target identification module is provided with a vehicle target identification model, and the inspection track fault detection module is provided with an inspection track fault detection model; in the follow-up method, the light PP-YOLO-Tiny algorithm is adopted to establish the inspection track fault detection model, so that the inspection track fault detection model can be put down at the edge computing gateway, and the data processing pressure of the data server is further reduced.
According to the tunnel traffic flow monitoring system based on the inspection robot, the inspection robot and the inspection track are adopted to monitor traffic flow in a tunnel, a manual mode is avoided, safety and accuracy are improved, tunnel traffic flow monitoring difficulty is reduced, labor cost input is reduced, meanwhile, data transmission can be achieved under the condition that no network exists by adopting the edge computing gateway, and data transmission efficiency is improved.
Example 2:
as shown in fig. 2, the present embodiment provides a tunnel traffic flow monitoring method based on an inspection robot, and a tunnel traffic flow monitoring system based on the inspection robot, including the following steps:
the method for establishing the vehicle target identification model and the inspection track fault detection model comprises the following steps:
acquiring a historical vehicle passing image data set and a historical inspection track image data set, and performing data set expansion and pretreatment on the historical vehicle passing image data set and the historical inspection track image data set to obtain an expanded vehicle passing training data set and an expanded inspection track training data set;
the data set expansion comprises the steps of turning, cutting, translating, enhancing contrast and the like of the image data;
establishing an initial vehicle target identification model based on a YOLOv5 algorithm, and establishing an initial inspection rail fault detection model based on a PP-YOLO-Tiny algorithm;
inputting the vehicle passing training data set into an initial vehicle target recognition model for optimization training to obtain an optimal vehicle target recognition model;
inputting the polling track training data set into an initial polling track fault detection model to obtain an optimal polling track fault detection model;
the network structure of the vehicle target identification model comprises a first input end, a backhaul module, a first detectionNeck module and a first Prediction module which are sequentially connected;
the first input end processes an input image by using a Mosaic data enhancement method;
the Backbone module comprises a Focus structure and a CSP structure;
the structure of the first detectionNeck module is an FPN + PAN structure;
the first Prediction module performs Loss calculation by using a GIOU _ Loss function;
the network structure of the inspection track fault detection model comprises a second input end, a MobileNetV3 module, a second detective Neck module, a detective head module and a second Prediction module which are sequentially connected;
the MobileNet V3 module is a lightweight network structure, combines deep separable convolution, invoked responses and Linear bottleeck and SE modules, and utilizes NAS neural structure search to search the configuration and parameters of the network;
the second DetectionNeck module adopts a PAN structure to aggregate feature information from top to bottom and applies a miss activation function;
the DetectionHead module adopts a depth separable convolution which is more suitable for a mobile terminal, has less parameters and operation cost compared with the conventional convolution operation, and is more suitable for the memory space and the computational power of the mobile terminal;
the method for detecting the faults of the inspection track comprises the following steps that the inspection track video data are infrared video data, and the inspection track fault detection model is used for carrying out fault detection on the inspection track according to the previous inspection track video data, and comprises the following steps:
frame interception and pretreatment are carried out on the last polling track video data to obtain the polling track video data after pretreatment of continuous frames;
sequentially inputting the preprocessed inspection track video data of the continuous frames into an inspection track fault detection model for target detection to obtain a plurality of inspection track fault target images;
carrying out fault detection on the plurality of routing inspection track fault target images to obtain a fault detection result;
the fault detection results comprise serious faults, moderate damages and slight scratches, the serious faults are that the inspection robot cannot normally run and pass due to breakage or serious damage of the inspection rail caused by long-term running or vehicle collision, the moderate damages are that the inspection rail has defects or scars which do not influence the passage of the inspection robot, but an alarm signal needs to be sent to inform inspection personnel to maintain, the slight scratches are shallower scars which can be developed into deeper scars or defects in the future, but are not processed at present;
if the serious fault exists, an alarm signal is sent out, and the tunnel traffic flow monitoring method is stopped, otherwise, the next step is carried out;
acquiring real-time vehicle passing video data and real-time polling track video data in a tunnel;
the vehicle passing video data is provided with a time label and is infrared video data, the real-time vehicle passing video data is preprocessed to obtain preprocessed vehicle passing image data of continuous frames, and the method comprises the following steps:
frame interception is carried out on the real-time vehicle passing video data to obtain initial vehicle passing image data of continuous frames;
carrying out denoising processing, gray level processing and normalization processing on the initial vehicle passing image data of the continuous frames to obtain preprocessed vehicle passing image data of the continuous frames;
sequentially inputting the preprocessed vehicle passing image data of continuous frames into a vehicle target recognition model for vehicle target recognition and tracking to obtain the number of vehicle targets, and the method comprises the following steps:
carrying out grid division on the preprocessed vehicle passing image data of all frames, and acquiring a priori frame of each grid;
acquiring an initial prediction frame corresponding to each prior frame according to the offset of the prior frame and a preset prediction frame;
carrying out non-maximum suppression screening on the initial prediction frame according to a preset intersection ratio and a preset confidence coefficient to obtain a final prediction frame of the preprocessed vehicle passing image data of all frames;
carrying out vehicle target identification on the image area in the final prediction frame to obtain a plurality of vehicle targets;
tracking and counting the same vehicle target by using a multi-target tracking algorithm to obtain the number of the vehicle targets;
calculating the traffic flow according to the target number of the vehicles in the sampling period to obtain a tunnel traffic flow result;
the vehicle passing video data is provided with the time labels, so that the number of passing vehicle targets in a sampling period can be obtained, the result of the tunnel traffic flow is obtained by dividing the number of the vehicle targets by the sampling period, and the sampling period is usually not more than 48 hours.
According to the tunnel traffic flow monitoring method based on the inspection robot, the vehicle target and the inspection track fault target are automatically detected and identified based on the deep learning algorithm, manual identification and statistics are avoided, the monitoring accuracy is improved, inspection track fault detection is performed between each tunnel traffic flow monitoring, the normal work of the inspection robot is guaranteed, and the practicability and the safety are improved.
The present invention is not limited to the above-described alternative embodiments, and various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. The utility model provides a tunnel traffic flow monitoring system based on robot patrols and examines which characterized in that: including patrolling and examining the robot, patrolling and examining track, edge calculation gateway and surveillance center, the robot of patrolling and examining set up in patrolling and examining the track top, the robot of patrolling and examining is provided with vehicle video acquisition unit towards the current one side of vehicle, and patrols and examines robot and edge calculation gateway communication connection, the track of patrolling and examining set up in inside one side of tunnel, edge calculation gateway set up in the inside top of tunnel, and edge calculation gateway and surveillance center communication connection.
2. The inspection robot-based tunnel traffic monitoring system according to claim 1, wherein: the inspection robot comprises a body, a mobile unit arranged at the bottom end of the body, a vehicle video acquisition unit arranged on one side of the body facing the vehicle to pass through, a track video acquisition unit arranged at one end of the body, and a robot main control unit and a rechargeable battery arranged in the body, wherein the robot main control unit is electrically connected with the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively, the robot main control unit is in communication connection with an edge computing gateway, the mobile unit is arranged at the top end of an inspection track, and the rechargeable battery is electrically connected with the robot main control unit, the mobile unit, the vehicle video acquisition unit and the track video acquisition unit respectively;
the robot main control unit comprises a robot main control module, a positioning module, a first storage module, a motor driving module and a wireless communication module, wherein the robot main control module is respectively and electrically connected with the positioning module, the first storage module, the motor driving module and the wireless communication module;
the vehicle video acquisition unit be first high-speed infrared camera, and first high-speed infrared camera sets up towards the current one side of vehicle, track video acquisition unit for setting up the high-speed infrared camera of second at the body both ends, the high-speed infrared camera orientation of second patrol and examine the track setting.
3. The inspection robot-based tunnel traffic monitoring system according to claim 2, wherein: the edge computing gateway comprises an edge computing unit and a network unit, the edge computing unit is electrically connected with the network unit, and the network unit is respectively in communication connection with a wireless communication module and a monitoring center of the inspection robot;
the edge calculation unit comprises an edge calculation main control module, a second storage module, an image preprocessing module and an encryption module, wherein the edge calculation main control module is respectively connected with the second storage module, the image preprocessing module, the encryption module and the network unit.
4. The inspection robot-based tunnel traffic monitoring system according to claim 3, wherein: the monitoring center is provided with a data server, the data server is in communication connection with the edge computing gateway, and the data server is in communication connection with an external cloud data center;
the data server comprises a data parallel receiving module, a decrypting module, a vehicle target identification module, a traffic flow calculating module, an inspection track fault detecting module, a cache database module and a data parallel uploading module, wherein the data parallel receiving module is respectively connected with the edge calculating gateway and the decrypting module, the decrypting module is respectively connected with the vehicle target identification module and the inspection track fault detecting module, the vehicle target identification module is connected with the traffic flow calculating module, the cache database module is respectively connected with the data parallel receiving module, the decrypting module, the vehicle target identification module, the traffic flow calculating module, the inspection track fault detecting module and the data parallel uploading module, and the data parallel uploading module is in communication connection with an external cloud data center;
the vehicle target identification module is provided with a vehicle target identification model, and the inspection track fault detection module is provided with an inspection track fault detection model.
5. A tunnel traffic flow monitoring method based on an inspection robot is based on the tunnel traffic flow monitoring system of claim 4, and is characterized in that: the method comprises the following steps:
establishing a vehicle target identification model and a routing inspection track fault detection model;
carrying out fault detection on the inspection track according to the last inspection track video data by using an inspection track fault detection model, if a serious fault exists, sending an alarm signal, and stopping a tunnel vehicle flow monitoring method, otherwise, entering the next step;
acquiring real-time vehicle passing video data and real-time polling track video data in a tunnel;
preprocessing the real-time vehicle passing video data to obtain preprocessed vehicle passing image data of continuous frames;
sequentially inputting the preprocessed vehicle passing image data of the continuous frames into a vehicle target recognition model for vehicle target recognition and tracking to obtain the number of vehicle targets;
and calculating the traffic flow according to the target number of the vehicles in the sampling period to obtain a tunnel traffic flow result.
6. The inspection robot-based tunnel traffic flow monitoring method according to claim 5, wherein: the method for establishing the vehicle target identification model and the inspection track fault detection model comprises the following steps:
acquiring a historical vehicle passing image data set and a historical inspection track image data set, and performing data set expansion and pretreatment on the historical vehicle passing image data set and the historical inspection track image data set to obtain an expanded vehicle passing training data set and an expanded inspection track training data set;
establishing an initial vehicle target identification model based on a YOLOv5 algorithm, and establishing an initial inspection rail fault detection model based on a PP-YOLO-Tiny algorithm;
inputting the vehicle passing training data set into an initial vehicle target recognition model for optimization training to obtain an optimal vehicle target recognition model;
and inputting the patrol rail training data set into an initial patrol rail fault detection model to obtain an optimal patrol rail fault detection model.
7. The inspection robot-based tunnel traffic flow monitoring method according to claim 6, wherein: the network structure of the vehicle target identification model comprises a first input end, a backhaul module, a first detectionNeck module and a first Prediction module which are sequentially connected;
the network structure of the inspection track fault detection model comprises a second input end, a MobileNet V3 module, a second detectionNeck module, a DetectionHead module and a second Prediction module which are sequentially connected.
8. The inspection robot-based tunnel traffic flow monitoring method according to claim 6, wherein: the method for detecting the faults of the patrol track by using the patrol track fault detection model according to the previous patrol track video data comprises the following steps:
frame interception and pretreatment are carried out on the last polling track video data to obtain the polling track video data after pretreatment of continuous frames;
sequentially inputting the preprocessed inspection track video data of the continuous frames into an inspection track fault detection model for target detection to obtain a plurality of inspection track fault target images;
carrying out fault detection on the plurality of routing inspection track fault target images to obtain a fault detection result;
the failure detection results include severe failure, moderate damage and mild bruising.
9. The inspection robot-based tunnel traffic flow monitoring method according to claim 6, wherein: the method for preprocessing the real-time vehicle passing video data comprises the following steps:
frame interception is carried out on the real-time vehicle passing video data to obtain initial vehicle passing image data of continuous frames;
and carrying out denoising processing, gray level processing and normalization processing on the initial vehicle passing image data of the continuous frames to obtain the preprocessed vehicle passing image data of the continuous frames.
10. The inspection robot-based tunnel traffic flow monitoring method according to claim 9, wherein: sequentially inputting the preprocessed vehicle passing image data of continuous frames into a vehicle target recognition model for vehicle target recognition and tracking, and comprising the following steps:
carrying out grid division on the preprocessed vehicle passing image data of all frames, and acquiring a priori frame of each grid;
acquiring an initial prediction frame corresponding to each prior frame according to the offset of the prior frame and a preset prediction frame;
performing non-maximum suppression screening on the initial prediction frame according to the preset intersection ratio and the preset confidence coefficient to obtain a final prediction frame of the preprocessed vehicle passing image data of all frames;
carrying out vehicle target identification on the image area in the final prediction frame to obtain a plurality of vehicle targets;
and tracking and counting the same vehicle target by using a multi-target tracking algorithm to obtain the number of the vehicle targets.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491758A (en) * | 2018-02-08 | 2018-09-04 | 深圳市睿灵创新科技开发有限公司 | A kind of track detection method and robot |
| CN109688388A (en) * | 2019-01-31 | 2019-04-26 | 宁波诠航机械科技有限公司 | A method of using the comprehensive real time monitoring of tunnel crusing robot |
| CN109935080A (en) * | 2019-04-10 | 2019-06-25 | 武汉大学 | The monitoring system and method that a kind of vehicle flowrate on traffic route calculates in real time |
| CN110085029A (en) * | 2019-05-31 | 2019-08-02 | 重庆交通大学 | Highway cruising inspection system and method based on rail mounted crusing robot |
| CN210850282U (en) * | 2019-08-02 | 2020-06-26 | 武庆珍 | Highway tunnel inspection robot |
| CN111815951A (en) * | 2020-07-17 | 2020-10-23 | 山东科技大学 | Road vehicle monitoring system and method based on intelligent vision Internet of things |
| CN112200483A (en) * | 2020-10-22 | 2021-01-08 | 卡斯柯信号有限公司 | Automatic inspection system and automatic inspection method for subway trackside equipment |
| CN112232237A (en) * | 2020-10-20 | 2021-01-15 | 城云科技（中国）有限公司 | Vehicle flow monitoring method, system, computer device and storage medium |
| CN112348201A (en) * | 2020-11-11 | 2021-02-09 | 扬州大学 | Intelligent decision implementation method for automatic driving group vehicle based on federal deep reinforcement learning |
| CN112665578A (en) * | 2021-03-16 | 2021-04-16 | 湖南承希科技有限公司 | Inspection positioning method and system based on tunnel inspection robot |
| CN112743559A (en) * | 2020-12-29 | 2021-05-04 | 上海市东方海事工程技术有限公司 | Suspension type tunnel inspection robot, system and method |
| CN113139495A (en) * | 2021-04-29 | 2021-07-20 | 姜冬阳 | Tunnel side-mounted video traffic flow detection method and system based on deep learning |
| CN113327248A (en) * | 2021-08-03 | 2021-08-31 | 四川九通智路科技有限公司 | Tunnel traffic flow statistical method based on video |
| CN113961222A (en) * | 2021-11-10 | 2022-01-21 | 交控科技股份有限公司 | Rail transit software remote maintenance method and device and storage medium |
| CN114023062A (en) * | 2021-10-27 | 2022-02-08 | 河海大学 | Traffic flow information monitoring method based on deep learning and edge calculation |
| CN114139570A (en) * | 2021-10-28 | 2022-03-04 | 中国科学院自动化研究所 | Distributed detection system based on active learning |
-
2022
- 2022-05-19 CN CN202210545940.3A patent/CN114973694B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491758A (en) * | 2018-02-08 | 2018-09-04 | 深圳市睿灵创新科技开发有限公司 | A kind of track detection method and robot |
| CN109688388A (en) * | 2019-01-31 | 2019-04-26 | 宁波诠航机械科技有限公司 | A method of using the comprehensive real time monitoring of tunnel crusing robot |
| CN109935080A (en) * | 2019-04-10 | 2019-06-25 | 武汉大学 | The monitoring system and method that a kind of vehicle flowrate on traffic route calculates in real time |
| CN110085029A (en) * | 2019-05-31 | 2019-08-02 | 重庆交通大学 | Highway cruising inspection system and method based on rail mounted crusing robot |
| CN210850282U (en) * | 2019-08-02 | 2020-06-26 | 武庆珍 | Highway tunnel inspection robot |
| CN111815951A (en) * | 2020-07-17 | 2020-10-23 | 山东科技大学 | Road vehicle monitoring system and method based on intelligent vision Internet of things |
| CN112232237A (en) * | 2020-10-20 | 2021-01-15 | 城云科技（中国）有限公司 | Vehicle flow monitoring method, system, computer device and storage medium |
| CN112200483A (en) * | 2020-10-22 | 2021-01-08 | 卡斯柯信号有限公司 | Automatic inspection system and automatic inspection method for subway trackside equipment |
| CN112348201A (en) * | 2020-11-11 | 2021-02-09 | 扬州大学 | Intelligent decision implementation method for automatic driving group vehicle based on federal deep reinforcement learning |
| CN112743559A (en) * | 2020-12-29 | 2021-05-04 | 上海市东方海事工程技术有限公司 | Suspension type tunnel inspection robot, system and method |
| CN112665578A (en) * | 2021-03-16 | 2021-04-16 | 湖南承希科技有限公司 | Inspection positioning method and system based on tunnel inspection robot |
| CN113139495A (en) * | 2021-04-29 | 2021-07-20 | 姜冬阳 | Tunnel side-mounted video traffic flow detection method and system based on deep learning |
| CN113327248A (en) * | 2021-08-03 | 2021-08-31 | 四川九通智路科技有限公司 | Tunnel traffic flow statistical method based on video |
| CN114023062A (en) * | 2021-10-27 | 2022-02-08 | 河海大学 | Traffic flow information monitoring method based on deep learning and edge calculation |
| CN114139570A (en) * | 2021-10-28 | 2022-03-04 | 中国科学院自动化研究所 | Distributed detection system based on active learning |
| CN113961222A (en) * | 2021-11-10 | 2022-01-21 | 交控科技股份有限公司 | Rail transit software remote maintenance method and device and storage medium |
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|---|---|
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