基于SIFT的中厚板表面缺陷识别方法
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摘要
中厚板在生产过程中,由于各种因素难免会产生压痕、辊印、划伤等缺陷,严重的缺陷会对下一道轧制工艺产生不良的影响,因此在包含氧化铁皮背景中准确识别出真实缺陷对提高钢铁企业的产品质量至关重要。该文采用尺度不变特征转换(scale-invariant feature transform,SIFT)算子来提取具有尺度旋转不变性的特征向量,并采用Euclidean距离相似性判定度量实现图像匹配,进而识别出中厚板表面缺陷。该文通过大量实验分析并确定各参数取值,最终将SIFT算法应用到中厚板表面缺陷识别,实验结果表明:该算法对辊印、压痕等缺陷的识别率较高,能够达到95%,尤其是对连续出现的缺陷检测效果明显,从而验证了SIFT方法较好的光照不变性、旋转不变性和仿射不变性。
Moderately thick plates have defects such as indentations,roll marks and scratches from the production processes.Since serious defects can negatively impact the next rolling process,operators must identify serious defects containing iron oxide defects on the surface to improve the quality of iron and steel products.A scale invariant feature transform(SIFT)operator was used to extract feature vectors that are scale rotation invariant.A Euclidean distance similarity measure is used for image matching to identify the surface defects of moderately thick plates.Many tests were then run to identify the proper values of each parameter.The SIFT algorithm then had a 95% surface defect recognition rate and was especially effective for continuous defects.Thus,this SIFT method which is unaffected by the illumination and is rotation and affine invariant gives excellent recognition of iron oxide defects.
Moderately thick plates have defects such as indentations,roll marks and scratches from the production processes.Since serious defects can negatively impact the next rolling process,operators must identify serious defects containing iron oxide defects on the surface to improve the quality of iron and steel products.A scale invariant feature transform(SIFT)operator was used to extract feature vectors that are scale rotation invariant.A Euclidean distance similarity measure is used for image matching to identify the surface defects of moderately thick plates.Many tests were then run to identify the proper values of each parameter.The SIFT algorithm then had a 95% surface defect recognition rate and was especially effective for continuous defects.Thus,this SIFT method which is unaffected by the illumination and is rotation and affine invariant gives excellent recognition of iron oxide defects.
引文
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[2]徐科,徐金梧.基于图像处理的冷轧带钢表面缺陷在线检测技术[J].钢铁,2002,37(12):61-64.XU K,XU J W.On-line inspection of surface defects of cold rolled strips based on image processing[J].Iron&Steel,2002,37(12):61-64.(in Chinese)
[3] LOWE D G.Object recognition from local scale-invariant features[C]//Proceedings of the 17th IEEE International Conference on Computer Vision.Kerkyra,Greece:IEEE,1999:1150-1157.
[4] LINDEBERG T.Feature detection with automatic scale selection[J].International Journal of Computer Vision,1998,30(2):79-116.
[5] WANG H X,YANG K J,GAO F,et al.Normalization methods of SIFT vector for object recognition[C]//Proceedings of the 10th International Symposium on Distributed Computing and Applications to Business,Engineering and Science.Wuxi,China:IEEE,2011:175-178.
[6] ZHAO W L,NGO C W.Flip-invariant SIFT for copy and object detection[J]. IEEETransactionsonImage Processing,2013,22(3):980-991.
[7] BELCHER C,DU Y Z.Region-based SIFT approach to iris recognition[J].Optics and Lasers in Engineering,2009,47(1):139-147.
[8] YUE H J,SUN X Y,WU F,et al.SIFT-based image compression[C]//Proceedings of 2012IEEE International Conference on Multimedia and Expo. Melbourne, VIC,Australia:IEEE,2012:473-478.
[9] HSU C Y,LU C S,PEI S C.Image feature extraction in encrypted domain with privacy-preserving SIFT[J].IEEE TransactionsonImageProcessing, 2012,21(11):4593-4607.
[10]OBESO F,GONZALEZ J A,BROWN A.Intelligent on-line surface inspection on a skinpass mill[J].Iron and Steel Engineer,1997(10):29-35.
[11]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.