Recognition of key components in mining equipment based on improved YOLOv4 algorithm
To enable the coal mine equipment detection platform to accurately and rapidly identify critical components of complex mining equipment,a lightweight improved model named GhostNet-YOLOv4 based on YOLOv4 is proposed.This model introduces GhostNet as the backbone of YOLOv4's CSPDarkNet53,which not only reduces the number of model parameters and redundant computations but also shortens the model training time.The shortcut structure in GhostNet mitigates network degradation and enhances the model's feature extraction capability.Experimental results demonstrate that the number of parameters in the improved mode is reduced by 83%compared to YOLOv4 and by 6%compared to MobileNetv3-YOLOv4,with an average recognition accuracy of 92.67%.The image detection speed can reach 33.75 frames per second,representing a 43%improvement over YOLOv4.Ghost-YOLOv4 effectively addresses the issues of slow detection speed and large model size in the task of detecting critical components of complex mining equipment.
万方数据
维普
