首页|基于改进Canny算子的齿轮边缘缺陷检测方法

基于改进Canny算子的齿轮边缘缺陷检测方法

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针对Canny算子在齿轮边缘缺陷检测中存在识别相似度低、因图像噪声影响导致微弱边缘难以提取、边缘连续性与鲁棒适应性差的问题,提出了一种改进Canny算子的齿轮边缘缺陷检测技术.改进滤波器,使用梯度双边滤波进行图片预处理,平滑图像并减少图像噪声;改进了卷积核,在45°和135°梯度方向上对像素点进行非极大值抑制,增加了微弱边缘被保留的概率;采用最大类间方差法(Otsu算法)计算图像的高阈值,通过双阈值的办法自适应寻找图像强弱边缘;将Prewitt算子和Canny算法以及改进算法进行对比实验验证.结果表明,改进算法可以提取更完整的齿轮边缘,处理后图片的峰值信噪比(PSNR)相比Canny算法提升了 16%,检测效果提升了30%,重叠系数高达81.9%,提升了25.1%,为齿轮边缘缺陷检测提供了一定的参考价值.
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

高昕、甄国涌、储成群、王子硕

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中北大学仪器与电子学院

梯度双边滤波 齿轮边缘缺陷检测 最大类间方差法 峰值信噪比

国家自然科学基金重点项目山西省基础研究计划

62131018202103021222012

2024

工具技术
成都工具研究所

工具技术

CSTPCD北大核心
影响因子:0.147
ISSN:1000-7008
年,卷(期):2024.58(9)