梁颖,詹光曹,徐科.基于二值化赋范梯度的中厚板表面缺陷检测[J].表面技术,2019,48(10):336-341.
LIANG Ying,ZHAN Guang-cao,XU Ke.Surface Defect Detection of Medium and Heavy Plates Based on Binarized Normed Gradients[J].Surface Technology,2019,48(10):336-341 |
| 基于二值化赋范梯度的中厚板表面缺陷检测 |
| Surface Defect Detection of Medium and Heavy Plates Based on Binarized Normed Gradients |
| 投稿时间：2019-02-27 修订日期：2019-10-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2019.10.041 |
| 中文关键词: 中厚板 缺陷检测 二值化赋范梯度（BING） ROI提取 规范化梯度（NG） 线性SVM |
| 英文关键词:medium plates defect detection Binarized Normed Gradients (BING) extraction of ROI Normed Gradients (NG) linear SVM |
| 基金项目:“十三五”国家重点研发计划课题（2018YFB0704304）；国家自然科学基金项目（51674031） |
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| Author | Institution |
| LIANG Ying | 1.Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China |
| ZHAN Guang-cao | 2.Medium Plate Mill, Fujian Sangang Minguang Co., Ltd, Sanming 365000, China |
| XU Ke | 1.Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China |
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| 中文摘要: |
| 目的 针对中厚板表面复杂、缺陷识别率低的问题，设计一种有效的候选窗口提取方法，提升中厚板表面缺陷检测的准确性与实时性。方法 引入视觉选择性注意机制，采用一种基于二值化赋范梯度特征（Binarized Normed Gradients，BING）的一般对象估计算法来快速准确地提取缺陷感兴趣区域（Region of Interest ，ROI），有效缩短搜寻过程。首先将样本归一化到8×8大小，提取规范化梯度特征（Normed Gradients，NG），学习一个测量显著性的线性SVM分类器来预测图像窗口含有缺陷的可能性。然后再通过样本尺度优化显著性评分，学习一个校准显著评分的线性SVM分类器。最后将两个SVM模型级联，用于在线检测，提取缺陷感兴趣区域。结果 将训练好的BING模型与Inception-V3卷积神经网络相结合，用于中厚板表面缺陷检测与识别，BING算法有效减少了ROI数量，在ROI数量为500的情况下，达到了98.2%的召回率。结论 在保证缺陷召回率的前提下，BING生成的ROI数量比滑动窗口遍历方式少2个数量级，有效减少了后续识别算法的计算量，有利于引入复杂的分类器提升中厚板表面缺陷识别的准确率。 |
| 英文摘要: |
| The work aims to design an effective candidate window extraction method to improve the accuracy and real-time of surface defect detection of medium plates for problems of complex surface and low defect recognition rate in medium plates. The visual selective attention mechanism was introduced and generic object estimation algorithm based on Binarized Normed Gradients (BING) was used to quickly and accurately extract the defect region of interest (ROI) and shorten the search process effectively. Firstly, the samples were normalized to 8×8 size, and Normed Gradients (NG) were extracted to learn a linear SVM classifier for measuring saliency to predict the possibility of defects in image windows. Then, a linear SVM classifier calibrating the saliency score was learned by optimizing the saliency score at the sample scale. Finally, two SVM models were cascaded for on-line detection and extraction of defect interest region. The trained BING model and Inception-V3 convolutional networks were combined for defect detection and identification in medium plates. BING algorithm effectively reduced the number of windows of interest and achieved a recall rate of 98.2% when the number of ROI was 500. Under the premise of ensuring defect recall rate, the number of ROIs generated by BING is two orders of magnitude smaller than the sliding window method, which effectively reduces the computational complexity of subsequent identification modules and is conducive to introducing complex classifiers to improve the accuracy of surface defect detection of medium plates. |
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