基于KRB-YOLOv5s的煤矸识别方法OA北大核心CSTPCD

To solve the problems of low recognition accuracy,missed detection,and false detection of coal and gangue caused by complex environmental factors such as high dust,low illumination,and high noise in coal mines,a recognition method for coal and gangue based on KRB-YOLOv5s is proposed.The K-means++algorithm is used to re-cluster the dataset to obtain more accurate anchor box parameters.The RepLKNet network is introduced into the YOLOv5s backbone network to extract higher-level feature information of the target through a large kernel convolutional architecture.A BiFPN network is introduced into the neck of YOLOv5s to quickly capture and fuse multi-scale features of the target through bidirectional cross scale connections.Experiments are conducted on a dataset of coal and gangue,and the results show that compared with other YOLO series detection algorithms,the KRB-YOLOv5s algorithm has the best comprehensive detection performance under high dust,low illumination,and high noise conditions,with an average recognition precision(mAP)of 94.5%,which is 3.3 percentage points higher than that of YOLOv5s algorithm.The research conclusions provide a reference for intelligent sorting of coal and gangue under complex working conditions in coal mine.