Railway foreign body intrusion detection based on Improved YOLOv4 network model
Yuan Li, Yan Xu, Chunmiao Wei · 2022
To solve the problem of low accuracy of detection and positioning of invading foreign body targets of the scene of railway foreign body intrusion, this paper proposes a railway foreign body intrusion detection method that optimizes the YOLOv4 model. First, the self-built railway foreign body intrusion limit dataset was expanded to solve the model over-fitting problem and improve the generalization ability of the model. Second, replace the backbone feature extraction network in the YOLOv4 model with a lightweight feature extraction structure, which effectively reduces the total number of parameters and deepens the number of network layers. Experiments show that the algorithm has good results and performance in detection accuracy and detection speed, effectively solving the problems of missed detection, misdetection, and low detection accuracy of foreign objects in the current road foreign object intrusion scene.