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Railroad transportation is an important infrastructure for public travel and cargo transportation. With the rapid development of railroad construction, the mileage of operation continues to increase and the road network continues to improve, and the railroad covers a wider range of terrain, making the environment for train travel more complex. The behavior of individuals or groups entering the railroad track without permission or authorization may cause serious harm to the life safety of personnel and the normal operation of railroad traffic. Therefore, this paper aimed to propose an improved YOLOV5-based track personnel intrusion detection algorithm, which improves the recall rate by 14% and reduces the loss rate by introducing the CBAM attention mechanism into the C3 layer of the three pyramid strata of YOLO, achieving an average precision of 98%. The results of experimental simulation using the improved model on the acquired image data to be detected for unauthorized personnel intrusion into the track show that the machine vision-based railroad track personnel intrusion detection algorithm in this paper takes full account of the characteristics of the railroad scenario, and the processing has a high detection precision. The finding of the study can make contribution to the Railway Bureau to effectively detect the risk of railroad safety and reduce the probability of accidents.
Yang et al. (Fri,) studied this question.