Research on coal gangue recognition method based on SFD-YOLOv5s

Abstract

ABSTRACT Aiming at the current coal gangue recognition model’s difficulty in recognizing aggregated coal gangue targets, which leads to the problems of misdetections, omissions, and inaccurate localization, we propose a coal gangue recognition method based on the SFD-YOLOv5s model. The SE attention mechanism is improved in incorporating maximum pooling and introduced into the backbone network of the YOLOv5s model to improve the model’s ability to extract the critical feature information of the coal gangue. Furthermore, to improve the recognition ability of the YOLOv5s model for aggregated targets, we introduce the Focal-EIoU loss function and the Dyhead. The experimental results show that the overall performance of the SFD-YOLOv5s model has surpassed other mainstream object detection models of the YOLO series. The SFD-YOLOv5s model has the highest precision, recall, and mean average precision. The precision reaches 97.9%, the recall is 96.2%, and the mean average precision is 94.5%, which is 8.7%, 8.9%, and 9.0% higher compared with the YOLOv5s model, respectively. The FPS is 96.15, which can fully meet the demand for real-time detection of coal gangue. Meanwhile, the SFD-YOLOv5s model has the best recognition effect of aggregated coal gangue targets.