摘要
利用FLANN算法对已检测的SURF描述子进行特征匹配,对获得的关键点进行迭代排序,用RANSAC算法进行匹配点优化提纯。通过灰度差分算法对经过矩阵变换配准后的图像进行缺陷区域提取,采用Otsu阈值法进行图像分割,并对缺陷连通域面积特征进行分析。试验结果表明,该方法在检测速度与范围方面优于传统的检测算法,在小模数塑料齿轮工业在线检测方面有着积极的研究意义与价值。
The FLANN（ Fast library for approximate nearest neighbors） algorithm is used to match the detected SURF（ Speeded up robust features） descriptors. The obtained key points are iteratively sorted,and the RANSAC（ Random sample consensus） algorithm is used to extract the matching points for two times. The gray difference algorithm is used to extract the defective region after the matrix transformation registration,using the Otsu threshold method to segment the image and the characteristics of defect area domain are analyzed,which uses the Otsu threshold method to segment the image. The results show that the method is superior to the traditional detection algorithm in detecting speed and range. So it has positive research significance and value in on-line inspection of small module plastic gear industry.
作者
杨亚
陶红艳
余成波
Yang Ya1,Tao Hongyan1 ,Yu Chengbo2(1 College of Mechanical Engineering,Chongqing University of Technology, Chongqing 400054, China;2 Remote Testing and Control Technology Research Institute, Chongqing University of Technology, Chongqing 400054, Chin)
出处
《机械传动》
CSCD
北大核心
2018年第5期156-160,共5页
Journal of Mechanical Transmission
基金
国家自然科学基金(61402063)
重庆市科技人才培养计划(新产品研发团队)(CSJ2013KJRC-TDJS40012)
关键词
小模数塑料齿轮
特征匹配
灰度差分
缺陷检测
连通域分析
Small modulus plastic gear
Feature matching Gray difference
Detection of defects
Connected domain analysis
