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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: As an infrastructure for urban development, it is particularly important to ensure the safe operation of urban rail transit. Foreign object intrusion in urban rail transit area is one of the main causes of train accidents. To tackle the obstacle detection challenge in rail transit, this paper introduces the CS-YOLO urban rail foreign object intrusion detection model. It utilizes the improved YOLOv5s algorithm, incorporating an enhanced convolutional attention CBAM module to replace the original YOLOv5s algorithm's backbone network C3 module. In addition, the KM-Decoupled Head is proposed to decouple the detection head, and SIoU is applied as the loss function. Tested on the WZ dataset, the average accuracy increased from 0.844 to 0.893 .The research method in this paper provides a reference basis for urban rail transit safety detection, which has certain reference value.
Shuangyuan Li, Zhengwei Wang, Yanchang Lv and Xiangyang Liu, “Improved Algorithm with YOLOv5s for Obstacle Detection of Rail Transit” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150142
@article{Li2024,
title = {Improved Algorithm with YOLOv5s for Obstacle Detection of Rail Transit},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150142},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150142},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Shuangyuan Li and Zhengwei Wang and Yanchang Lv and Xiangyang Liu}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.