Key Point Estimate Network for Rail-Track Detection

• Published in: IEEE transactions on intelligent transportation systems Vol. 25; no. 5; pp. 4077 - 4088
• Main Authors: Yang, Songyue, Wang, Zhangyu, Yu, Guizhen, Liu, Wentao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accidents; dislocation assignment; Lighting; Obstacle avoidance; pseudo-attention; Rail transportation; Rail-track detection; rail-track generalized IoU; Rails; Railway tracks; Roads; Task analysis; Transportation
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3327996

Rail-track detection is a crucial function for an active obstacle avoidance system in trains. However, existing methods face challenges in effectively detecting rail-tracks, particularly in turnout scenarios. This study introduces a novel rail-track detection approach using a key-point estimate network. The network treats the rail-track as a pair and constructs a dedicated model for detection. Additionally, a pseudo-attention mechanism leverages the detection output from previous stages, enabling the network to focus on the rail-track region. Also, a dislocation assignment mechanism is proposed to address label assignment confusion at turnouts. Moreover, a rail-track generalized IoU is also introduced, treating the rail-track as a pair and adds a correction term to enhance detection performance. Experimental results demonstrate that the proposed method achieves a remarkable mF1 score of 69.42%, establishing it as the state-of-the-art (SOTA) in this field. Furthermore, the effectiveness of the proposed method has been validated and applied in real-world testing on the Hong Kong Metro Tsuen Wan Line.