Research on coal and gangue identification method based on improved YOLOv3

The identification technology of coal and gangue is of great significance to realize the automatic separation of coal and gangue, but the existing image identification algorithm can not meet the actual needs in practicability and accuracy. Based on image processing technology and deep learning technology, a coal and gangue identification method based on improved YOLOv3 was proposed. The network structure and loss function of the original YOLOv3 were improved according to the problems of small recognition target and low identification of coal and gangue, and the identification test was carried out on the test set with the model generated by training. The test results show that the improved YOLOv3 can make the model converge quickly in a short time on a small sample. The identification time of single image is 21.6 ms, and the identification accuracy is 95.4%. It can adapt to the coal and gangue samples in different environments and realize real-time detection and identification.