Fast identification model for coal and gangue based on the improved tiny YOLO v3

Hongguang Pan, Yuhong Shi, Xinyu Lei, Zheng Wang, Fangfang Xin

Abstract: In the process of coal mining, the coal quality is greatly reduced due to the mixing of gangue, and the stacking and burning of coal and gangue can cause serious environmental pollution. The fast identification of coal and gangue is an important technology for gangue sorting. Considering tiny YOLO v3 has the advantages of fast running speed, simple network and good effectiveness, an improved tiny-YOLO-v3-based fast identification model is proposed in this paper, which including the spatial pyramid pooling (SPP) net, the squeeze-and-excitation (SE) module and the dilated convolution. Firstly, the SPP net preprocesses the input images into acceptable size via a single convolution layer. Then, the SE module, which can strengthen the attention among channels in RGB image, is adopted to capture the key information and enhance the sensitivity of network accurately. Finally, the dilated convolution, which can enlarge the receptive filed without increasing parameters, is used to further optimize and realize the fast identification of coal and gangue. The experiments show that, compared with the tiny YOLO v3 the average intersection over union (Avg IOU) of proposed model is as high as 0.39%, the required time for each image and the loss reduced by 7.41 and 53.01%, respectively. The mean Average Precision (mAP@0.5) of the proposed network is 3.12% higher than Faster RCNN, reaching 0.964. It is an efficient fast identification model for gangue-sorting.