| 摘 要: 针对金属表面缺陷检测中存在的图像失真、构造分类器精确度不高及系统计算量大的问题，现提出一种高质量的基于支持向量机的金属表面缺陷检测方法。采用形态学方法对图像进行预处理，通过融合GLCM与HOG特征提取到的结果建立较为完备的缺陷模型，便于后期构造高精度分类器。最后，利用OTSU算法进行阈值分割，通过计算连通分量个数等方法进行缺陷分析。相较于一般的缺陷检测方法，该检测方法准确率达到96.67%，提高了缺陷检测的效率。 |
| 关键词: 缺陷检测；图像处理；SVM分类器 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 江苏省产学研合作项目(BY2020626). |
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| A Metal Surface Defect Detection Method based on Support Vector Machine |
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GU Aihua, LI Wenhan, WANG Zhengqian, YIN Zuohao, YE Kaining, CHEN Yu
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(College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)
guaihua1978@163.com; liwenhan1127@163.com; 2794965946@qq.com; 2693704323@qq.com; 2506331929@qq.com; 7042799@qq.com
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| Abstract: Aiming at the problems of image distortion in metal surface defect detection, inaccuracy of construction classifier and large amount of system calculation, this paper proposes a high-quality metal surface defect detection method based on support vector machine. Morphological methods are used to preprocess the image, and a relatively complete defect model is established by fusing the results extracted from GLCM (Gray-level Co-occurrence Matrix) and HOG (Histogram of Oriented Gradient) features, which is convenient for constructing a high-precision classifier in the later stage. Finally, OTSU algorithm is used to perform threshold segmentation, and defect analysis is performed by calculating the number of connected components. Compared with general defect detection methods, the proposed detection method improves the efficiency of defect detection and its accuracy rate is as high as 96.67%. |
| Keywords: defect detection; image processing; SVM (Support Vector Machine) classifier |
