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Coal is an indispensable energy source for humans. As an important part of the mining industry, intelligent separation of coal and gangue will promote development. The traditional methods of recognition do not consider the interference created by a dynamic environment. There are many problems such as noise, complex backgrounds and occlusion, which lead to low accuracy and cannot satisfy real-time requirements in mining. Aiming at dynamic environments, a real-time multilevel fusion recognition system was built in this paper. First, we introduced a near-infrared camera into the field of separation, which was used to form a binocular system with a visible light camera. The SVM classifier was obtained by feature selection and fusion training of the binocular system, which overcomes the interference of environmental factors. Then, we proposed a new deep learning training method of two-sample fusion to improve the recognition network performance by expanding the number of samples and features. Finally, the SVM and deep learning algorithms were combined to establish a fast detection strategy. In addition, the length suppression algorithm was added to solve the occlusion problem. The accuracy of the fusion algorithm was 0.923 and the detection speed was increased to 26 fps. The experimental results indicated that the sorting system satisfied the requirements of real-time and robust of the coal industry.

A Real-Time Multilevel Fusion Recognition System for Coal and Gangue Based on Near-Infrared Sensing