Intelligent Detection of Foreign Matter in Coal Mine Transportation Belt Based on Convolution Neural Network

This paper applies the CenterNet target detection algorithm to the foreign object detection of coal conveying belts in coal mines. Given the fast running speed of coal conveying belts and the influence of background and light sources on the objects to be inspected, an improved algorithm of CenterNet is proposed. First, the depth separable volume is introduced. The product replaces the standard convolution, which improves the detection efficiency. At the same time, the normalization method is optimized to reduce the consumption of computer memory. Finally, the weighted feature fusion method is added so that the features of each layer are fully utilized, and the detection accuracy is improved. The experimental results show that the improved algorithm has improved speed and accuracy compared with the original CenterNet algorithm. The foreign object detection algorithm proposed in this paper mainly detects coal gangue and can also detect iron tools such as bolts, drill bits, and channel steel. In the experimental environment, the average detection rate is about 20fps, which can meet the needs of real-time detection.