Belt conveyors often experience longitudinal tearing of the conveyor belt,which affects the safety and production efficiency of workers.This article proposes an improved detection method based on YOLOv5 to address the issue of longitudinal tearing of conveyor belts in belt conveyors.The lightweight network MobileNetv3 is used instead of CSPDarknet as the backbone network for feature extraction,integrating a lightweight attention model SE to enhance the model's learning of target weights and enhance the backbone network's ability to learn and process features.In addition,a deep separable convolutional module is also used,Effectively reducing the number of parameters in the network structure.This study also addresses the issue of mismatch between the real box and the predicted box,replacing the original bounding box regression loss function GIOU with a more efficient SIOU,further improving the speed of model training and the accuracy of inference.Finally,the shortcomings and deficiencies of the dataset were optimized using data augmentation.The experimental results show that compared to the original model,the improved model not only reduces the number of parameters but also improves the detection speed and accuracy.
YOLOv5image detectionlongitudinal tearlightweightloss function
维普
万方数据
