基于改进ORB-FLANN算法的工件图像识别方法  

Workpiece Image Recognition Method Based on Improved ORB-FLANN Algorithm

作　　者：朱志浩 鹿志旭 郭毓[2] 高直 ZHU Zhihao;LU Zhixu;GUO Yu;GAO Zhi(School of Electrical Engineering,Yancheng Institute of Technology,Yancheng 224051,China;School of Automation,Nanjing University of Technology,Nanjing 210000,China;School of Information Engineering,Yancheng Institute of Science and Technology,Yancheng 224051,China)

机构地区：[1]盐城工学院电气工程学院,江苏盐城224051 [2]南京理工大学自动化学院,江苏南京210000 [3]盐城工学院信息工程学院,江苏盐城224051

出　　处：《电子科技》2024年第4期55-61,共7页Electronic Science and Technology

基　　金：国家自然科学基金(61973167);盐城工学院校级科研项目(XJR2020041)。

摘　　要：针对传统图像识别算法匹配正确率低、运行时间较长等问题,文中提出了基于改进ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)的工件图像识别方法。对ORB算法特征描述、图像特征匹配算法进行修改,解决传统图像识别算法在图像存在尺度和旋转变换情况下存在的弊端并降低误匹配率。该方法对ORB算法检测到的特征点采用SURF(Speeded Up Robust Features)算法添加方向信息并完成特征描述,得到旋转尺度不变性的特征点,结合FLANN算法并引入双向匹配策略进行特征点粗匹配,最后利用渐进采样一致算法进一步剔除误匹配点对完成精匹配。实验结果表明,与其他方法相比,改进算法在处理尺度、旋转等变换图像时,匹配正确率分别提高了2.6%~18.8%和29.5%~43.9%,运行时长均在4 s以内,提高了对工件图像的识别效率和精准性。In view of the problems of low matching rate and long running time of traditional image recognition algorithms,an improved ORB-FLANN(Oriented FAST and Rotated BRIEF-Fast Library for Approximate Nearest Neighbors)based workpiece image recognition method is proposed.The feature description of ORB algorithm and image feature matching algorithm are modified to solve the disadvantages of traditional image recognition algorithm in the case of scale and rotation transformation and reduce the mismatching rate of matching.For the feature points detected by ORB algorithm,SURF(Speeded Up Robust Features)algorithm is used to add orientation information and complete the feature description,so as to obtain the feature points with rotation-scale invariance.FLANN algorithm is combined with bidirectional matching strategy for coarse matching of feature points.Finally,the progressive sampling-congruence algorithm is used to further eliminate the mismatched point pairs and complete the fine matching.The experimental results show that compared with other methods,the improved algorithm can improve the matching accuracy of 2.6%~18.8%and 29.5%~43.9%,respectively,when processing scale and rotation transform images,and the running time is within 4s,improving the efficiency and accuracy of workpiece image recognition.

关 键 词：图像识别 ORB算法 SURF算法 FLANN算法 双向匹配 渐进采样一致 匹配正确率 工件图像 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]