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Railway Intrusion Behavior Detection Method Based on Faster R-CNN

A detection method and behavioral technology, applied to alarms, instruments, and biological neural network models that rely on broken/disturbed straightened ropes/metal wires, etc., can solve the problems of low detection accuracy and achieve the goal of reducing false alarms Probability, the effect of improving the detection accuracy

Active Publication Date: 2022-06-07
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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  • Railway Intrusion Behavior Detection Method Based on Faster R-CNN
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  • Railway Intrusion Behavior Detection Method Based on Faster R-CNN

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Embodiment Construction

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

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

[0045] Step 1) Build a 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-module includes sequentially cascaded DAS vibration detection optical cables, optical signal demodulation The host and the monitoring terminal analysis host, the DAS vibration detection optical cable is laid along the railway fence, and contains N sampling points distributed at equal intervals; the output end of the monitoring terminal analysis host is connected to the data processing sub-module, where N≥2, this embodiment , deploy the optical fiber distributed acoustic sensing DAS sub-module on ...

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Abstract

The present invention proposes 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. The implementation steps are: constructing a DAS data processing system; obtaining training samples Set and test sample set; construct railway intrusion behavior detection network model Faster R-CNN; iteratively train railway intrusion behavior detection network model Faster R-CNN; obtain railway abnormal intrusion behavior detection results. The network model Faster R-CNN constructed by the present invention uses the normalized spatio-temporal signal image as the training sample set, fully combines the spatio-temporal characteristics of the signal, distinguishes the interference of the background noise signal, reduces false positives, and at the same time, the candidate area generates a network accurate prediction feature map The position of the region candidate frame improves the detection accuracy to a certain extent and can be used to protect the 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 intrusion behavior of railways, in particular to a method for detecting abnormal intrusion behaviors of railways based on Faster R-CNN. It can be used for the safety monitoring of the railway perimeter to protect the safe operation of railway trains. Background technique [0002] With the development of railways and intelligent transportation systems, the speed and convenience of transportation have been brought to people, and the security work along railway lines has become more and more important. Dangerous or malicious intrusions such as illegally destroying or crossing the railway fence, stealing railway fence cables, etc., will cause serious accident hazards to the safety of train operation, bring economic losses to people, and may also cause traffic congestion in some areas. related casualties. The abnormal railway intrusio...

Claims

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Application Information

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Patent Type & Authority Patents(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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