Research on conveyor belt deviation detection method

The machine vision-based conveyor belt deviation detection methods detect conveyor belt edge features. The features contain false edges. The existing research is difficult to identify false edges and has poor adaptability to multiple scenes. To solve this problem, the region of interest (ROI) is extracted from the conveyor belt monitoring image and normalized. The Canny algorithm with a larger threshold range is used to extract edge feature points to improve the scene adaptability of the algorithm. Morphological filtering methods are used to deal with some impurities and false edges. For images where the Canny algorithm cannot detect effective edges, gamma transform and gradient filtering in the 45° and 135° directions are performed on the extracted ROI to enhance edge features. The feature point extraction and morphological filtering based on the Canny algorithm are carried out. The pixel value relationship of edge points, neighborhood features, compactness features, as well as the length, relative position, and slope of edge lines are taken as constraints. The line filtering and sorting algorithm based on the idea of divide and conquer search is used to filter and fit the extracted edge feature points to obtain a real-time edge of the conveyor belt. The pixel value of the real-time edge is subtracted from the pixel value of the edge when no deviation occurs, and the pixel value of the current deviation is obtained. The test results show that for the conveyor belt monitoring images under various scenes, the detection error of the conveyor belt deviation detection method based on the Canny algorithm and line filtering and sorting is less than three pixel values. The detection time of 100 images is 6.9451 s. The CPU occupancy of the edge computer processing four video images is 132%, which meets the accuracy and real-time requirements of on-site conveyor belt edge detection.