Conveyor belt tear detection algorithm based on improved YOLOv5s

As one of the main components of the belt conveyor, conveyor belt often works in relatively harsh environment and is prone to longitudinal tearing failures. Therefore, adequate and timely detection of tearing failures is crucial is necessary. In order to improve the accuracy and efficiency of traditional conveyor belt tear detection methods, a real-time conveyor belt tear detection algorithm based on the improved YOLOv5s network is proposed. Introduced multi head channel attention (MCA) attention mechanism into the original model to enhance the ability to model channel relationships between feature maps; this article also improves new loss functions, namely Alpha-iou Loss and Scale-Invariant Overlap Union Loss (SIOULOSS), which can better handle the problems including sample imbalance and long-tail distribution in target detection, thereby further improving the performance of the model.