Recognition of coal and gangue under low illumination based on SG-YOLO model

Abstract

ABSTRACT For the low illumination and dust in the coal and gangue identification site environment, which leads to poor recognition, an improved lightweight low-illumination gangue recognition algorithm based on YOLOv5s model is proposed: SG-YOLO algorithm. The original backbone network is replaced by GhostNet, a lightweight network, to optimize the feature extraction structure, reduce the model parameters, and decrease the computational power of the model; the SimAM attention mechanism module is introduced in the head part of the model to enhance the learning ability of coal and gangue features. Experiments show that compared with the YOLOv5s model, the improved model has a mAP of 97.0% on the gangue dataset, which improved by 1.4%. The size of the model is compressed to 55% of the original. The number of parameters is reduced by 47.6%, and the computational effort is reduced by 49.4%. Meanwhile, the recognition accuracy of the improved SG-YOLO model for coal and gangue under low illumination is 96.5% and 98.5% respectively, which effectively improves the recognition accuracy of coal rain gangue under low illumination environment.