[1]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.
Copy

Defect detection on a steel slab surface based on the characteristics of an image’s saliency region

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 1 Page number: 75-80 Column: 学术论文—机器感知与模式识别 Public date: 2012-02-25
Title:
Defect detection on a steel slab surface based on the characteristics of an image’s saliency region
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
Keywords:
steel slab surface;  defect detection;  saliency region;  feature extraction;  Gabor wavelet;  Adaboost classifier
CLC:
TP391.4
DOI:
-
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. 
References:
［1］MA Yufei, ZHANG Hongjiang. Contrastbased image attention analysis by using fuzzy growing［C］//Proceedings of the 11th ACM Intemational Conference on Multimedia. New York, USA: ACM, 2003: 374381.
?［2］KO B C, KWAK S Y, BYUN H. SVMbased salient region(s) extraction method for image retrieval［C］//Proceedings of the 17th International Conference on Pattern Recognition. Washington, DC, USA: IEEE Computer Society, 2004: 977980.
［3］ITTI L, KOCH C, NIEBUR E. A model of saliencybased visual attention for rapid scene analysis［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 12541259.
［4］GOFERMAN S, ZELNIKMANOR L, TAL A. Contextaware saliency detection［C］//2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA， 2010: 23762383.
［5］ZHAI Y, SHAH M. Visual attention detection in video sequences using spatiotemporal cues［C］//Proceedings of the 14th Annual ACM International Conference on Multimedia. New York, USA: ACM, 2006: 815824.
［6］CHENG Mingming, ZHANG Guoxin, MITRA N J, et al. Global contrast based salient region detection［C］//2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 409416.
［7］钟微,谢雪梅,石光明.一种灵活的图像频谱分割与去噪方法［J］.西安电子科技大学学报, 2007, 34(6): 935938.
ZHONG Wei, XIE Xuemei, SHI Guangming. Image spectrum segmentation and denoising based on multichannel nonuniform filter banks［J］. Journal of Xidian University, 2007, 34(6): 935938.
［8］张红梅,卞正中,郭佑民,等.感兴趣区域高效提取算法［J］.软件学报, 2005, 16(1): 7788.
ZHANG Hongmei, BIAN Zhengzhong, GUO Youmin, et al.〖LL〗An efficient approach to extraction of region of interest［J］. Journal of Software, 2005, 16(1): 7788.
［9］HOU Xiaodi, ZHANG Liqing. Saliency detection: a spectral residual approach［C］//2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA, 2007: 18.
［10］毕为民,孙才新,姚陈果.基于门限值小波包变换抑制局部放电白噪干扰［J］.重庆大学学报, 2003, 26(6): 5355.
?BI Weimin, SUN Caixin, YAO Chenguo. Wavelet packet decomposition based on entropy threshold for whitenoise rejection in PD signal［J］. Journal of Chongqing University, 2003, 26(6): 5355.
Similar References:

Memo

-

Last Update: 2012-05-07

Copyright © CAAI Transactions on Intelligent Systems