# Coal Ganguue Target Detection Based on Improved YOLOv5s
###### Abstract  
Coal ganguue sorting is a necessary process in coal mine production, and removing ganguue is the basis for the coal production of clean energy; it is also an important approach to reduce the cost of washing, improve the grade of finished coal and increase the economic efficiency of coal mining enterprises. For the problem of high similarity and low-degree dynamic recognition of coal and gangue, a coal ganguue target detection method based on improved YOLOv5s is proposed. Based on the YOLOv5s network, the decoupled head and SimAM attention mechanism are introduced and the CSP module in the neck part of YOLOv5s is replaced with the VoV-GSCSP structure. The experimental results show that the proposed method improves the mAP value by 6.1% over YOLOv5s in the gangue target detection task, while maintaining a higher detection speed. The coal ganguue classification precision reaches 99.7% when tested on 1479 images. Compared with YOLOv5 series, YOLOv7 series, SSD and Faster-RCNN, the proposed method invariably yields higher precision and detection speed to meet the requirements of real-time detection. The experiments prove that the method proposed in this paper can be applied to the coal ganguue sorting industry for fast and high-precision identification of coal ganguue. coal ganguue; target detection; YOLOv5s; decoupled head; attention mechanism +
Footnote †: journal:of the gangue, which will reduce the recognition precision. \(\gamma\)-ray for the recognition of gangue has strong anti-interference ability and the recognition precision is higher, but the damage to the human body is life-threatening. The image recognition method features simple equipment, a high degree of automation, high recognition efficiency and strong portability. With the development of artificial intelligence technology in recent years, artificial intelligence is being integrated in more and more fields; image recognition, as an important application field of artificial intelligence, has undergone rapid development. Combining machine learning technology and artificial intelligence for coal gangue target detection can solve the problems of low recognition precision and unstable recognition, and is the future development trend of intelligent sorting of coal gangue. At present, the relevant research on coal gangue image recognition mainly includes two categories: image-based traditional methods and deep learning methods. Image processing and machine learning have achieved certain results in the task of coal gangue recognition; machine learning can be based on the feature matrix extracted from image processing for category classification, which is of great significance in the field of coal gangue sorting. There are scholars who constantly propose and improve some non-contact gangue selection methods. [PERSON] et al. used X-ray transmission imaging of coal and gangue differently to achieve coal gangue sorting [5]; despite the realization of being non-contact, the method has been limited by radiation hazards. The grey scale symbiotic matrix is a statistically based texture feature extraction method, which provides information about the direction of the image grey scale, spacing and the magnitude of the change. [PERSON] et al. used this method for the study of coal gangue identification; the shortcomings of this method are that it does not fully capture the graphical characteristics of the local grey scale, and so the effect is less than ideal for local identification [6]. Numerous researchers have used image processing and machine learning methods to bring progress to coal gangue sorting to a large extent [7; 8; 9; 10; 11; 12; 13], but these methods generally have the demerits of complex data preprocessing, detection of a single dimension, recognition of low precision, poor generalization and so on.