Research on Coal Gangue Detection Method Based on YOLO-RepGFPN Model
The existing coal gangue identification methods based on deep learning are prone to miss and misdetect under the interference of dim light,high noise,occlusion and other factors.In order to solve this problem,a coal gangue detection method based on YOLO-RepGFPN model was pro-posed.Based on the YOLOV5s model,the YOLO-RepGFPN model is improved:1)The CA attention mechanism is introduced into the Backbone part to make the network make full use of the rich context information and enhance the feature learning ability.2)the RepGFPN network module is used in the Neck part to enhance the model's ability to extract and fuse the high-level semantics and low-level spatial features of gangue,so as to improve the detection accuracy.3)Modify the loss function to XIOU to enhance the positioning accuracy and robustness in the process of target matching and target border regression.The experimental results show that the RepGFPN gangue identification method has the highest recognition accuracy(mAP)of 94.7%compared with the current mainstream YOLO algo-rithm,which can effectively avoid the phenomenon of missed detection,false detection and re-detec-tion that are easy to occur in the identification of coal gangue under harsh conditions.
coal gangue identificationdeep learningattention mechanismsRepGFPNloss function