Image Edge Detection Method Based on Improved Canny Algorithm
A modified Canny edge detection technique is proposed to address the issues of low recognition similarity,difficulty in extracting weak edges due to image noise,and poor edge continuity and robustness in gear edge defect detec-tion.The filter is improved by gradient bilateral filtering for image preprocessing,which smoothens the image and reduces image noise.The convolution kernel is enhanced by adding a non-maximum suppression process for pixel points in the 45° and 135° gradient directions,increasing the probability of retaining weak edges.The maximum between-class variance meth-od(Otsu's algorithm)is employed to compute the high threshold of the image,and an adaptive dual-threshold method is used to locate strong and weak edges in the image.A comparative experiment is conducted between the Prewitt operator,the traditional Canny algorithm,and the modified algorithm.The results show that the modified algorithm can extract more com-plete gear edges,with a 16%improvement in peak signal-to-noise ratio(PSNR)compared to Canny,a 30%improvement in detection effectiveness,and an overlap coefficient of 81.9%,which is a 25.1%enhancement.This provides valuable ref-erence for gear edge defect detection.
gradient bilateral filteringgear edge detectionmaximum between-class variance methodpeak signal-to-noise ratio