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Faster R-CNN-based railway abnormal intrusion behavior detection method

A detection method and behavioral technology, applied in the field of intelligent transportation, can solve the problems of low detection accuracy, reduce the probability of false positives, and improve the detection accuracy

Active Publication Date: 2021-10-22
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects in the above-mentioned prior art, and propose a method for detecting abnormal railway intrusion behavior based on Faster R-CNN, which is used to solve the technical problem of low detection accuracy in the prior art

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  • Faster R-CNN-based railway abnormal intrusion behavior detection method
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  • Faster R-CNN-based railway abnormal intrusion behavior detection method

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Embodiment Construction

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] refer to figure 1 , the present invention comprises the following steps:

[0045] Step 1) Construct DAS data processing system:

[0046] Construct a DAS data processing system including cascaded optical fiber distributed acoustic sensing DAS sub-modules and data processing sub-modules, wherein the optical fiber distributed acoustic sensing DAS sub-modules include sequentially cascaded DAS vibration detection optical cables, optical signal demodulation The main engine and the monitoring terminal analysis main engine, and the DAS vibration detection optical cable are laid along the railway fence, including a total of N sampling points distributed at equal intervals; the output end of the monitoring terminal analysis main engine is connected to the data processing sub-module, where N≥2, this embodiment Among them, the optical fi...

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Abstract

The invention provides a Faster R-CNN-based railway abnormal intrusion behavior detection method, which is used for solving the technical problem of low detection accuracy in the prior art. The method comprises the following implementation steps: constructing a DAS data processing system; obtaining a training sample set and a test sample set; constructing a railway intrusion behavior detection network model Faster R-CNN (Convolutional Neural Network); carrying out iterative training on the railway intrusion behavior detection network model Faster R-CNN; and obtaining a railway abnormal intrusion behavior detection result. According to the constructed network model Faster R-CNN, a normalized space-time signal image is used as a training sample set, space-time features of signals are fully combined, interference of background noise signals is distinguished, false alarms are reduced, meanwhile, the candidate region generation network accurately predicts the region candidate frame position of a feature map, the detection accuracy is improved to a certain degree, and the method can be used for protecting safe operation of railway trains.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and relates to a method for detecting abnormal railway intrusion behaviors, in particular to a method for detecting abnormal railway intrusion behaviors based on Faster R-CNN. It can be used for safety monitoring of the railway perimeter to protect the safe operation of railway trains. Background technique [0002] As the development of railways and intelligent transportation systems brings people fast and convenient transportation, security work along railway lines is becoming more and more important. Dangerous or malicious intrusions such as illegally destroying or crossing railway fences, stealing railway fence cables, etc., will cause serious accident hazards to train operation safety, bring economic losses to people, and may also cause traffic congestion in some areas. related casualties. Abnormal railway intrusion behavior is a behavior that threatens the safety of rail...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V8/10G06N3/04G08B13/12
CPCG01V8/10G08B13/124G06N3/045
Inventor 惠一龙马鑫蕊肖潇李长乐段江华
Owner XIDIAN UNIV
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