Real-time detection and classification of coal and gangue based on X-ray radiogram and YOLOv8

Coal is an important part of the world's energy structure and gangue is mined along with coal. The extremely high visual similarity between them makes the sorting process more difficult. Existing studies are mainly based on feature extraction or the non-end-to-end convolutional neural networks to classify the target in images. Although some of these methods can have excellent accuracy, have problems with complex feature extraction processes and long image processing time. In this paper, I propose a method for detecting and classifying coal and gangue based on an X-ray map and the YOLOv8 model. The experimental results show that this method greatly improves the speed of image processing and ensures high classification accuracy, which can better solve the problem of real-time detection of coal and gangue. It has a good effect in the real-time detection and classification task.