Automatic Obstacle Detection Method for the Train Based on Deep Learning

Qiang Zhang, Fei Yan (), Weina Song, Rui Wang and Gen Li
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Qiang Zhang: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Fei Yan: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Weina Song: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Rui Wang: School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Gen Li: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2023, vol. 15, issue 2, 1-14

Abstract: Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.

Keywords: rail area detection; obstacle detection; rail traffic; deep convolutional neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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