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Method for monitoring tunnel operation state based on deep neural network

A deep neural network and operating state technology, applied in the computer field, can solve problems such as affecting the life of the road surface, inaccurate detection, damage to the ground sensing coil, etc., to reduce the calculation of data and increase the accuracy.

Active Publication Date: 2022-05-13
四川九通智路科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The construction process of the ground sense coil has a great impact on reliability and life, and the road surface needs to be cut, which affects the life of the road surface
Traffic needs to be interrupted when the coil is installed and repaired, and the ground induction coil is easily damaged by heavy vehicles, road repairs, etc.
In addition, the maintenance workload of the coil is relatively large. Generally, the coil needs to be replaced after 2-3 years of use. The actual maintenance cost is higher than other speed measuring equipment.
The current video detection method needs to track all the vehicles in the vehicle detection frame to judge whether the vehicle in the monitoring image is consistent with the vehicle in the previous frame of monitoring image, and the calculation is complicated. At the same time, due to the double lane overtaking, etc. operation, making detection inaccurate
It is also possible to judge whether it is the same vehicle by detecting the license plates of all vehicles in the surveillance image. Due to the small size of the distant vehicles in the surveillance image, vehicle recognition is sometimes inaccurate.

Method used

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  • Method for monitoring tunnel operation state based on deep neural network
  • Method for monitoring tunnel operation state based on deep neural network
  • Method for monitoring tunnel operation state based on deep neural network

Examples

Experimental program
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Embodiment 1

[0065] like figure 1 As shown, the embodiment of the present invention provides a method for monitoring the running state of a tunnel based on a deep neural network, the method comprising:

[0066] S101: Obtain a first monitoring image; the first monitoring image is a monitoring image collected by monitoring equipment in front of the tunnel;

[0067] S102: Based on the first monitoring image and the vehicle detection algorithm, obtain a first set of vehicle detection frames; the first set of vehicle detection frames includes a plurality of vehicle detection frames in the first monitoring image; the vehicle detection frames include The position of the center point of the vehicle detection frame, the width and height of the vehicle detection frame; the position of the center point of the vehicle detection frame represents the position of the center point of the vehicle detection frame in the first monitoring image;

[0068] Wherein, the vehicle detection algorithm adopts the YO...

Embodiment 2

[0137] Based on the above-mentioned method for monitoring the running state of a tunnel based on a deep neural network, an embodiment of the present invention also provides a system for monitoring the running state of a tunnel based on a deep neural network. The system includes an acquisition module, a vehicle frame acquisition module, and a front vehicle frame An acquisition module and a traffic flow acquisition module.

[0138] Wherein, the collection module is used to collect the first monitoring image and the second monitoring image. The first monitoring image is a monitoring image collected by the monitoring equipment in front of the tunnel. The second monitoring image is a monitoring image collected by the monitoring equipment in front of the tunnel N seconds after the first monitoring image is collected.

[0139] Wherein, the vehicle frame obtaining module is used to obtain the first vehicle detection frame set and the second vehicle detection frame set. Based on the ...

Embodiment 3

[0144] The embodiment of the present invention also provides an electronic device, such as image 3 As shown, it includes a memory 504, a processor 502, and a computer program stored in the memory 504 and operable on the processor 502. When the processor 502 executes the program, it realizes the aforementioned monitoring tunnel based on a deep neural network. Run the steps of any method of the state's method.

[0145] Among them, in image 3 In, bus architecture (represented by bus 500), bus 500 may include any number of interconnected buses and bridges, bus 500 will include one or more processors represented by processor 502 and various types of memory represented by memory 504 circuits linked together. The bus 500 may also link together various other circuits, such as peripherals, voltage regulators, and power management circuits, etc., which are well known in the art and thus will not be further described herein. The bus interface 505 provides an interface between the bu...

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Abstract

The invention discloses a method for monitoring the running state of a tunnel based on a deep neural network, and the method comprises the steps: detecting a vehicle in a monitoring image, which is smaller than a foremost vehicle threshold value, and enabling the vehicle not to complete a complete overtaking operation in the range of the foremost vehicle threshold value, according to the method and the system, the condition that the foremost vehicle changes but the vehicle does not pass due to overtaking is avoided, the foremost vehicle in the two lanes is obtained and only the foremost vehicle is judged, data calculation is reduced, meanwhile, only the foremost clear vehicle is recognized during color recognition and license plate recognition, and the accuracy of vehicle recognition is greatly improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method for monitoring the running state of a tunnel based on a deep neural network. Background technique [0002] At present, highways use ground sensing coils, infrared lasers and video detection methods to detect vehicles. The construction process of the ground sense coil has a great impact on the reliability and life, and the road surface needs to be cut, which affects the life of the road surface. Traffic needs to be interrupted when the coil is installed and repaired, and the ground induction coil is easily damaged by heavy vehicles, road repairs, etc. In addition, the maintenance workload of the coil is relatively large. Generally, the coil needs to be replaced after 2-3 years of use. The actual maintenance cost is higher than other speed measuring equipment. The current video detection method needs to track all the vehicles in the vehicle detection frame to j...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/065G08G1/017G06V20/52G06V30/148G06V20/62G06V10/22G06V10/25G06V10/56G06N3/04G06N3/08
CPCG08G1/0125G08G1/0137G08G1/065G08G1/0175G06N3/08G06N3/045
Inventor 邓承刚张煜陈宇王廷梅代李涛刘义才童兴彬
Owner 四川九通智路科技有限公司
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