基于光度学特征与SVM的热轧铝板表面缺陷检测  

Surface Defect Detection of Hot-rolled Aluminum Sheet Based on Photometric Features and SVM

作　　者：吴旭东 闫志鸿[1] 胡飞涛 程健鹏 徐昊[1] WU Xudong;YAN Zhihong;HU Feitao;CHENG Jianpeng;XU Hao(Engineering Research Center for Advanced Manufacturing Technology of Automotive Structural Components,Department of Materials and Manufacturing,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学材料与制造学部,汽车结构部件先进制造技术教育部工程研究中心,北京100124

出　　处：《热加工工艺》2023年第6期157-162,共6页Hot Working Technology

基　　金：国家自然科学基金面上项目(51975015)。

摘　　要：工业产品的表面缺陷对其美观度和使用性能有重要影响。为实现热轧铝合金板材表面缺陷的自动检测与分类,提出一种基于光度学特征和支持向量机分类的机器视觉检测方法。针对铝合金板材的压痕、划痕、污点、擦伤、坑蚀等5种常见缺陷,依据其光度立体学特征,设计了相应的图像处理算法。可靠地检测出了缺陷区并提取了缺陷的几何形状特征和HSV特征。将得到的特征参数作为支持向量机的输入,设计了对应的分类器。结果表明:所设计的SVM分类器可较为准确地检测出表面缺陷类型,识别正确率为96.5%。The surface defects of industrial products have an important impact on the aesthetic and performance.In order to realize the automatic detection and classification of surface defects of hot-rolled aluminum alloy sheets,a machine vision detection method was proposed based on photometric feature extraction and support vector machine classification.Aiming at five common defects such as indentation,scratches,stains,bruises,and pitting of aluminum alloy plates,corresponding image processing algorithms were designed according to their photometric stereoscopic characteristics.The defect area was reliably detected and the geometric shape and HSV feature of the defect were extracted.The obtained feature parameters were used as the input of the support vector machine,and the corresponding classifier was designed.The results show that the designed SVM classifier can be accurately detected by the type of surface defect,with the recognition rate of 96.5%.

关 键 词：铝板缺陷检测 多特征提取 光度立体学 支持向量机 

分 类 号：TG166.3[金属学及工艺—热处理]