Tear detection method of conveyor belt based on fully convolutional neural network

The existing conveyor belt tear detection methods have problems, such as poor underground visible light imaging quality, lack of tear physical size measurement means, and poor generalization capability. In order to solve these problems, a conveyor belt tear detection method based on fully convolutional neural network is proposed. The method collects images based on a line-structured light imaging principle, and can effectively solve the problem of poor lighting conditions in a coal mine. The improved maximum method is used to detect line laser stripes, which can effectively eliminate the breakpoints of stripes, accurately extract stripes, and fit the missing points. The U-net network in the fully convolutional neural network is selected to segment the line laser stripe. The tear detection problem is converted into a semantic segmentation problem. The U-net network is optimized through dimension reduction, so as to reduce the number of parameters and calculations. The segmentation result is back-projected to the original image. The physical size of the tear is measured using the line-structured light calibration data. The experimental results show that the improved maximum method can effectively deal with the breakpoint area of line laser stripes without false detection and missed detection. The performance is superior to the Steger method and gray-weighted centroid method. The convergence speed of the U-net network is faster than that of the SegNet and FCNs network. The iteration stability is strong, and the evaluation index is optimal. The performance of the U-net4 network is better than that of U-net3 and U-net5. The test results on the verification set show that the recall rate of tear detection is 96.09%, and the precision is 96.85%. The measurement results on the experimental platform show that the maximum relative error of tear physical dimension measurement is −13.04%.