Conveyor belt tear detection method based on lion group optimization two-dimensional Otsu algorithm

The conveyor belt tear detection methods based on traditional two-dimensional Otsu segmentation algorithm adopts threshold selection method of exhaustive search. The image segmentation has a long time and poor real-time performance, which cannot meet requirements of tear detection accuracy and speed at the same time. For the above problems, a conveyor belt tear detection method based on lion group optimization two-dimensional Otsu was proposed. Firstly, the conveyor belt image is collected by the conveyor belt tear detection device, median filtering and histogram equalization were used to denoise and enhance the collected image to highlight the torn part. Then, the close to actual segmentation threshold of preprocessed image is obtained by lion group optimization two-dimensional Otsu algorithm, and the conveyor image is segmented by this threshold. Finally, the tear diagnosis is performed by calculating the number of black pixels in the segmented image. The simulation results show that the optimization ability of proposed method is more powerful than the traditional two-dimensional Otsu algorithm, and segmentation effect of conveyor belt is better, the cracks can be accurately segmented from the conveyor belt image. The correct rate of tear recognition of the method is 98.2%, improves the efficiency and shortens the running time, which can meet the accuracy and real-time requirements of the conveyor belt tear detection.