[1]吴家伟,严京旗,方志宏,等.基于图像显著性特征的铸坯表面缺陷检测[J].智能系统学报,2012,7(1):75-80.
　WU Jiawei,YAN Jingqi,FANG Zhihong,et al.Defect detection on a steel slab surface based on the characteristics of an image’s saliency region[J].CAAI Transactions on Intelligent Systems,2012,7(1):75-80.
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基于图像显著性特征的铸坯表面缺陷检测

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第1期 页码: 75-80 栏目: 学术论文—机器感知与模式识别 出版日期: 2012-02-25
Title:
Defect detection on a steel slab surface based on the characteristics of an image’s saliency region
文章编号:
1673-4785(2012)01-0075-06
作者:
吴家伟1,严京旗1,方志宏2,夏勇2,陆敏健3
1.上海交通大学 图像处理与模式识别研究所,上海 200030；
2.宝山钢铁股份有限公司研究院，上海 201900；
3.宝山钢铁股份有限公司 设备部，上海 201900
Author(s):
WU Jiawei1， YAN Jingqi1， FANG Zhihong2， XIA Yong2， LU Minjian3
1.Institute of Image Processing & Pattern Recognition， Shanghai Jiao Tong University， Shanghai 200030, China；
2.Institute of Baoshan Iron & Steel Co., Ltd., Shanghai 201900, China；
3.Equipment Department of Baoshan Iron & Steel Co., Ltd., Shanghai 201900,China
关键词:
铸坯表面; 缺陷检测; 显著性区域; 特征提取; Gabor小波; Adaboost分类器
Keywords:
steel slab surface;  defect detection;  saliency region;  feature extraction;  Gabor wavelet;  Adaboost classifier
分类号:
TP391.4
文献标志码:
A
摘要:
针对钢铁铸坯表面检测的缺陷复杂性问题，从图像处理及图形特征角度提出一种基于显著性区域特征的算法.该算法首先对源图像进行显著性特征区域处理和Gabor小波滤波处理，得到了对应的特征图像；然后再将2幅图像中的特征区域进行融合，得到可信度较高的缺陷特征区域图像；最后在缺陷区域中用训练好的Adaboost分类器检测缺陷，得到最终的缺陷定位结果.该算法结合了显著性特征和Gabor小波特征，既缩小了Adaboost分类器的搜索范围，也提高了排除伪缺陷的能力，具有较快的定位速度和较高的准确率.实验结果表明，该算法能获得较好的效果，具有较高的实用价值.
Abstract:
In considering complex defect conditions in steel slab surface detection, a new defect detection method based on the saliency region was presented from the viewpoint of image processing and graphics features. First, by the processing of saliency region characteristics and Gabor wavelet filtering, the feature image was obtained, and then the characteristic regions in the two images were fused to obtain a highly reliable image of the defect region characteristics. Finally, the defect was detected by a welltrained Adaboost classifier in the fused defect region, thereby obtaining the final defect positioning result. The algorithm combines saliency region characteristics and Gabor wavelet features; it not only narrows the search range of the Adaboost classifier, but also improves the ability to exclude pseudodefects. Consequently, it has faster positioning speed and higher accuracy. The algorithm performed well in the experiment and possesses high practical value. 
参考文献/References:
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备注/Memo

收稿日期：2011-11-23.
基金项目：国家自然科学基金资助项目（60873137）.
通信作者：严京旗.????????E-mail：jqyan@sjtu.edu.cn.
?作者简介：
吴家伟，男，1988年生，硕士研究生，主要研究方向为图像处理、机器学习与生物特征识别.
严京旗，男，1975年生，副教授.参与并主持了多项国家自然科学基金项目.主要研究方向为图像图形综合技术、可视计算、三维生物特征识别等.
?方志宏，男，1968年生，副教授，博士.主要研究方向为冶金自动化、图像处理、信号处理和电子技术的应用等.

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