A new railway foreign object intrusion detection method

Liyan Zhang; Haiyang Hao; Zhengang Lang; Liu Fang

doi:10.1117/12.3033131

5 July 2024 A new railway foreign object intrusion detection method

Liyan Zhang, Haiyang Hao, Zhengang Lang, Liu Fang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318476 (2024) https://doi.org/10.1117/12.3033131
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The intrusion of foreign objects into railways poses a great threat to the reliability and safety of railway systems. In order to effectively avoid such phenomena, this paper studies a method for detecting foreign objects in railway tracks based on deep learning principles. This method mainly includes two parts: rail detection and foreign object recognition. The rail detection part adopts a deep learning method based on UNet network semantic segmentation, which optimizes the convolutional structure of the UNet network into depthwise separable convolutions and inserts a spatial pyramid structure to improve the detection speed The foreign object recognition part adopts YOLOv5's deep learning method, which improves its detection accuracy by changing the loss function to SIoU, and adds a CA attention mechanism to determine the coordinates of the target and recognize and classify foreign objects. The experimental results show that the average recognition accuracy of the algorithm proposed in this paper on the dataset of railway foreign objects reaches 93.7%, which is 5.1% higher than the traditional YOLOv5 algorithm, indicating that the algorithm effectively improves the accuracy of railway foreign object intrusion recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liyan Zhang, Haiyang Hao, Zhengang Lang, and Liu Fang "A new railway foreign object intrusion detection method", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318476 (5 July 2024); https://doi.org/10.1117/12.3033131

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KEYWORDS

Object detection

Detection and tracking algorithms

Convolution

Image segmentation

Deep learning

Object recognition

Evolutionary algorithms

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