Coal gangue sorting based on deep learning

Abstract

Coal gangue sorting is an important link in the process of coal mining and processing, which can effectively reduce the difficulty and cost of coal post-processing. Aiming at the problems of complicated sorting process and low sorting efficiency of coal gangue, a coal gangue sorting method based on deep learning was proposed. The method is based on the YOLO v7 deep learning algorithm, and it achieves real-time detection of coal gangue by creating a coal gangue dataset and training the detection model. By constructing a coal gangue sorting platform, the capture of target gangue has been achieved. The experimental results show that the mAP of YOLO v7 model is 96.70%, and the detection speed is 69fps, which has significant advantages compared to YOLO v5, SSD and Faster RCNN algorithms.