基于机器视觉的废旧有色金属碎料识别方法  

Recognition Method of Waste Non-Ferrous Metal Fragments Based on Machine Vision

作　　者：章振原 秦训鹏 李轶峰 Zhang Zhenyuan;QinXunpeng;Li Yifeng(Hubei Key Laboratory of Advanced Technology for Automotive Component,School of Automotive Engineering,Wuhan University of Technology,Wuhan,Hubei 430070,China)

机构地区：[1]武汉理工大学汽车工程学院现代汽车零部件技术湖北省重点实验室,湖北武汉430070

出　　处：《激光与光电子学进展》2020年第16期186-193,共8页Laser & Optoelectronics Progress

基　　金：湖北省重大创新计划(2015AAA014)。

摘　　要：针对废旧有色金属碎料来源复杂、分选难度大、识别准确率低等问题,提出一种基于机器视觉的有色金属碎料分选方法。通过颜色矩与Tamura纹理特征,建立基于主成分分析(PCA)的支持向量机(SVM)优化分选算法,从机器视觉角度上提出了新的高精度自动化分选思路。结果表明,基于颜色、纹理特征的SVM算法能够有效地对金属碎料进行快速识别和分类,准确率达93.89%,并且提高了识别速度,满足对废金属进行大规模高效分选的要求。This paper proposes a sorting method for non-ferrous metal fragments based on machine vision to overcome the problems of complicated source,difficult sorting,and low recognition accuracy rate of waste non-ferrous metal fragments.Using color moments and Tamura texture characteristics,the optimized support vector machine(SVM)sorting algorithm based on principal component analysis(PCA)is established.Thus,a new concept of high-precision automatic sorting from the machine vision perspective is proposed herein.Results show that the proposed SVM algorithm based on color and texture features can effectively detect and classify metal fragments with an accuracy of 93.89%and improve the recognition speed.The proposed algorithm meets the requirements of large-scale and efficient separation of scrap metals.

关 键 词：图像处理 有色金属 颜色 纹理 主成分分析 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]