In-vehicle image technology for identifying faults of pantograph

DING Jian-ming; ZHOU Jing-yao; JIANG Hai-fan

doi:10.19818/j.cnki.1671-1637.2023.03.013

Volume 23 Issue 3

Jun. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(3): 173-187

DING Jian-ming, ZHOU Jing-yao, JIANG Hai-fan. In-vehicle image technology for identifying faults of pantograph[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 173-187. doi: 10.19818/j.cnki.1671-1637.2023.03.013

Citation:

DING Jian-ming, ZHOU Jing-yao, JIANG Hai-fan. In-vehicle image technology for identifying faults of pantograph[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 173-187. doi: 10.19818/j.cnki.1671-1637.2023.03.013

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In-vehicle image technology for identifying faults of pantograph

doi: 10.19818/j.cnki.1671-1637.2023.03.013

1.
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
2.
AVIC Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610073, Sichuan, China

Funds:

National Key Research and Development Program of China 2020YFA0710902

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Author Bio:
DING Jian-ming(1981-), male, associate professor, PhD, fdingjianming@home.swjtu.edu.cn
Received Date: 2022-12-22
Available Online: 2023-07-07
Publish Date: 2023-06-25

Abstract

Abstract

In view of the problem that the operation safety of the train was affected by pantograph faults, an in-vehicle image technology for identifying pantograph faults was proposed to detect the dropping, deformation and destruction of pantograph, the abnormal wear and notch of carbon contact strip, and deformation and loss faults of pantograph horns in real time. Based on the faster region-convolutional neural network (Faster R-CNN) target detection framework, a target detection model for locating the pantograph bow images was designed, and the residual network was used to replace the original convolutional network. The candidate region recommendation network was constructed by using the feature pyramid multi-scale prediction structure, so as to accurately and quickly locate the pantograph bow and detect the status. Based on the mask region-convolutional neural network (Mask R-CNN) instance segmentation framework, a pantograph bow image segmentation model was designed, and the network structure and feature map size of the detection head were redesigned to adapt to the slender and curved features of the pantograph, so as to accurately and quickly segment the pantograph bow image. In order to identify and locate faults more quickly in the segmented binary image, a rapid template matching strategy for the faults of pantograph horn and carbon contact strip was formulated according to the pantograph structure size and the position coordinates output by the image segmentation model. On this basis, detailed fault detection algorithms and procedures were compiled. Research results show that on the corresponding dataset, the average detection accuracy and average detection time per frame of the target detection model for positioning the pantograph bow images are 0.944 and 0.029 s, respectively. The average segmentation accuracy and average detection time per frame of the pantograph bow image segmentation model are 0.967 and 0.031 s, respectively. In addition, the detection accuracy and average detection time per frame of the template matching are 0.985 and 0.005 s, respectively. The average detection accuracy and average detection time per frame of the fault detection algorithm are 0.966 and 0.051 s, respectively. Thus, the proposed detection algorithm has high reliability and real-time performance.
- vehicle engineering,
- pantograph,
- fault detection,
- deep learning,
- target detection,
- instance segmentation,
- template matching

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References(30)

References

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In-vehicle image technology for identifying faults of pantograph

doi: 10.19818/j.cnki.1671-1637.2023.03.013

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In-vehicle image technology for identifying faults of pantograph

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