An algorithm for detecting the end of railway tracks based on the improved YOLOv5s

GENG Hao; LI Shaobin; SHENG Xueqing

doi:10.11860/j.issn.1673-0291.20240091

Abstract：In response to the problem that the on-board personnel monitoring the rail end of the steel rail transportation train have difficulty in judging whether the rail has detached from the fastening de-vice in a timely manner,a rail end detection algorithm based on the improved YOLOv5s is proposed.Firstly,the lightweight GhostNet backbone network is adopted to replace the original Cross Stage Par-tial Network(CSPNet),reducing the high requirements of the model for hardware resources;Sec-ondly,BiFomer and Receptive-Field Attention(RFA)attention mechanism are added to weaken the irrelevant background regions while improving the positioning ability of the rail end;Thirdly,the loss function SIoU is used to replace the original CIoU,enhancing the generalization ability of the model,and enabling the model to converge faster.Finally,the algorithm is verified and evaluated from the as-pects of detection accuracy and detection speed,and compared with algorithms such as Single Shot MultiBox Detector(SSD)and YOLOv8.The research results show that the improved detection algo-rithm achieves an average detection accuracy of 91.7％for the rail end detection,with an average de-tection time of 24 ms,which is 5.3％higher than the original YOLOv5s model.The missed detection and false detection situations have been significantly improved.The improved algorithm can achieve precise detection of the rail end in different environments,has good adaptability in adverse environ-ments such as dim lighting,and has a lower floating-point operation per second,which can be de-ployed in the embedded device RK3399 to better meet the real-time detection requirements of the rail end.