# Coal Ganguue Target Detection Based on Improved YOLOv5s
###### Abstract
Deep learning has developed faster in recent years, compared with other recognition methods; coal gangue sorting based on deep learning has the advantages of being non-contact with high security, high data utilization and high recognition precision and speed, and it constitutes the main research direction in the field of coal gangue target detection. [PERSON] used ResNet-50 and Soft-NMS to improve Faster R-CNN to improve the model's detection and localization of coal and gangue. The input image needs to go through the feature extraction region recommended to the candidate region, and then the output needs to be classified; thus, the detection speed is slow and cannot meet the real-time requirements in the practical situations [14]. [PERSON] et al. used migration learning to train the collected coal and gangue dataset; the precision of the validation set was only 82.5%, and the performance of the model needs to be improved [15]. [PERSON] et al. used a heat map as the standard image and established a CGR-CNN coal gangue recognition model; the model training precision reached 99.36% and the validation precision reached 95.09% [16], but it relied on the effect of the preprocessing of the image. [PERSON] et al., based on the target detection network YOLOv4, used a Laplacian operator and Gaussian filter to control for the effects of dust and impact on the image, and the recognition precision was improved by 1%-2% on the basis of the original network [17]. [PERSON] et al. used migration learning to improve the AlexNet feature extraction network and combined it with the RPN network to obtain the classification information and pixel coordinates of the coal and gangue; the precision of the model detection was 90.17% [18]. [PERSON] et al. used a U-shaped complete convolutional neural network to segment the coal gangue, and used the probability map output from this network to obtain the location and shape information of the coal gangue, which was only effective in specific environments and could not be used when the geological conditions of the coalfield varied greatly [19]. [PERSON] et al. proposed an intelligent separation method of coal gangue based on multispectral imaging technology and target detection, which detects the coal gangue with the improved target detection model YOLO v4, and accurately identifies coal and coal gangue [20]. Compared with the traditional image recognition method, the deep learning method can be used to improve the recognition precision of coal gangue sorting. This paper improves the network structure on the basis of the YOLOv5s deep learning network, introduces Slim-neck, the SimAM attention mechanism and decoupled head, and proposes the decoupled Head-GSConv-VoV-GSCSP-YOLOv5s-SimAM (DGSV-YOLOv5s-SA) network, with the following advantages:  
1. While maintaining a high detection speed in the field of coal gangue detection, the precision is significantly improved over the original network. 2. It pays more attention to the global features and increases the receptive field. 3. It provides an important reference for the coal gangue sorting industry. This paper is structured as follows: The introduction is given in Section 1. The pre-improvement deep learning network YOLOv5s and the improved model DGSV-YOLOv5s-SA proposed in this paper are presented in Section 2. The experimental platform, experimental metrics and dataset of this paper are provided in Section 3. Finally, the conclusion is given in Section 4.