Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (10): 2405-2418.doi: 10.13229/j.cnki.jdxbgxb20210266

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Detection of foreign object intrusion in railway region of interest based on lightweight network

Yong CHEN1,2(),Chen-tao LU1,Zhen WANG1   

  1. 1.School of Electronic and Information Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
    2.Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing，Lanzhou Jiaotong University，Lanzhou 730070，China
  • Received:2021-03-31 Online:2022-10-01 Published:2022-11-11

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Abstract:

For the problems of false warning， low detection efficiency and insatiable lightweight deployment of railway foreign object intrusion algorithms based on computer vision， a method of detection foreign object intrusion in railway region of interest based on lightweight network is proposed.. Firstly， the railway track line is detected by perspective transformation and cubic function fitting. By finding the area where the rail is located， and then expanding the division of dangerous area and safe area， the region of interest of railway foreign object intrusion detection is obtained. Secondly， a lightweight railway foreign object detection model is constructed by using sparse and channel pruning methods to compress YOLOv3 model. Finally， through the railway data set and field test， it shows that proposed method has high detection accuracy and detection speed. The lightweight model parameter space is reduced to 1/5 of the original， and the detection speed is 3.4 times of Faster R-CNN method， and which is 1.3 times of YOLOv3 method. It can quickly and effectively detect the intrusion of railway foreign object in dangerous areas of different railway scenario， and reduce the error warning.

Key words: computer application, intrusion detection, region of interest division, lightweight network, railway

CLC Number: 

  • TP391.4

Fig.1

Diagram of track edge extraction"

Fig.2

Diagram of perspective transformation"

Fig.3

Diagram of railway track perspective transformation"

Fig.4

Diagram of fitting railway track"

Fig.5

Region of interest division"

Fig.6

Diagram of batch normalization layer"

Fig.7

Channel pruning graph of proposed lightweight model"

Fig.8

Flow chart of proposed lightweight model"

Table 1

Comparison between YOLOv3 and proposed lightweight model"

模 型

模型尺寸

/MB

参数

个数

准确率/%

检测速度

/（帧·s-1）

YOLOv32346163143485.4345
本文模型461197295485.3958

Fig.9

Comparison of detection results under shadow condition"

Fig.10

Comparison of detection results under adverse light condition"

Fig.11

Comparison of detection results under close shot condition"

Fig.12

Comparison of detection results under perspective condition"

Fig.13

Comparison of detection results of railway crossing"

Fig.14

Comparison experiment 1 of detection results of railway curve"

Fig.15

Comparison experiment 2 of detection results of railway curve"

Table 2

Comparison of test results of each algorithm on foreign object intrusion test sets"

算 法准确率/%综合精度/%

检测速度

/（帧·s-1）

YOLOv3模型85.4375.4345
文献［8］方法86.3876.4726
文献［9］方法88.3277.5217
本文方法85.4175.2658

Fig.16

Detection experiment of common railway foreign object intrusion"

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