Sponsor:  Ke-Chen Song

This website is mainly used to present the research progress of vision-based inspection system for steel surface defects.

Welcome all interested students, teachers and the business units to academic exchange, and carry out a deeper cooperation.

    E-mail:unkechen@gmail.com , songkc@me.neu.edu.cn

Relevant International Companies and Conference:

                     More...

Relevant Database:

NEU surface defect database          Micro surface defect database        Oil pollution defect database    More...

Relevant Publications (International Journal Papers):

2020                                                                                                               
1
. Hongwen Dong,  Kechen Song, Yu He, Jing Xu, Yunhui Yan, Qinggang Meng. PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection [J]. IEEE Transactions on Industrial Informatics, 2020,16(12),7448-7458.  (paper)

2.Yu He,  Kechen Song, Qinggang Meng and Yunhui Yan. An End-to-end Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features [J]. IEEE Transactions on Instrumentation and Measuremente, 2020,69(4),1493-1504.    (paper)

3. Defu Zhang,  Kechen Song, Jing Xu, Yu He and Yunhui Yan. Unified Detection Method of Aluminium Profile Surface Defects: Common and Rare Defect Categories [J]. Optics and Lasers in Engineering, 2020, 126.  (paper)

4. Gao, Yiping, et al. A semi-supervised convolutional neural network-based method for steel surface defect recognition. Robotics and Computer-Integrated Manufacturing, 2020,61,101825.(paper)

5. Qiwu Luo, Xiaoxin Fang, Li Liu, Chunhua Yang, Yichuang Sun. Automated Visual Defect Detection for Flat Steel Surface: A Survey. IEEE Transactions on Instrumentation and Measuremente2020,69(3),626-644.(paper)

6. Qiwu Luo, Xiaoxin Fang,  et al.. Automated Visual Defect Classification for Flat Steel Surface: A Survey. IEEE Transactions on Instrumentation and Measuremente2020,69(12),9329-9349.(paper)

7. Guorong Song,  Kechen Song and Yunhui Yan. Saliency detection for strip steel surface defects using multiple constraints and improved texture features [J]. Optics and Lasers in Engineering, 2020, 128, 106000.  (paper)

8. Guorong Song,  Kechen Song and Yunhui Yan. EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects [J]. IEEE Transactions on Instrumentation and Measuremente, 2020,69(12),9709-9719.  (paper)(code) (SD-saliency-900 database)

9.Menghui Niu,  Kechen Song, Liming Huang, Qi Wang, Yunhui Yan, Qinggang Meng. Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images [J]. IEEE Transactions on Industrial Informatics, 2020. (paper) (code)(RSDDS-113 dataset)

 

2019                                                                                                               

1. Yu He, Kechen Song, Hongwen Dong, Yunhui Yan. Semi-supervised Defect Classification of Steel Surface Based on Multi-training and Generative Adversarial Network. Optics and Lasers in Engineering, 2019.(paper)

2. Yun, Jong Pil, et al. Automatic defect inspection system for steel products with exhaustive dynamic encoding algorithm for searches. Optical Engineering, 2019, 58.2: 023107.(paper)

3. He, Di,  et al. Surface defect classification of steels with a new semi-supervised learning method. Optics and Lasers in Engineering, 2019, 117 : 40-48.(paper)

4. He, Di, Ke Xu, and Peng Zhou. Defect detection of hot rolled steels with a new object detection framework called classification priority network. Computers & Industrial Engineering, 2019, 128 : 290-297.(paper)

5. Luo, Qiwu, et al. Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns. IEEE Access, 2019.(paper)

6. Gong R, Chu M, et al. A multi-class classifier based on support vector hyper-spheres for steel plate surface defects. Chemometrics & Intelligent Laboratory Systems, 2019.(paper)

7. Heying Wang, et al. A simple guidance template-based defect detection method for strip steel surfaces. IEEE Transactions on Industrial Informatics, 2019.(paper)

8. Fu, Guizhong, et al. A deep-learning-based approach for fast and robust steel surface defects classification. Optics and Lasers in Engineering, 2019, 121 : 397-405.(paper)

9. Wang, Jianzhu, et al. Surface Defect Detection via Entity Sparsity Pursuit with Intrinsic Priors. IEEE Transactions on Industrial Informatics, 2019.(paper)

                                                                                   

2018                                                                                                               
1. Gong R, Wu C, Chu M. Steel surface defect classification using multiple hyper-spheres support vector machine with additional information. Chemometrics & Intelligent Laboratory Systems, 2018, 172: 109-117.(paper)

2. Chu M, Liu X, Gong R, et al. Multi-class classification method using twin support vector machines with multi-information for steel surface defects. Chemometrics & Intelligent Laboratory Systems, 2018, 176: 108-118.(paper)

3. Xu K, Xu Y, Zhou P, et al. Application of RNAMlet to surface defect identification of steels. Optics and Lasers in Engineering, 2018, 105: 110-117.(paper)

4. Liu Y, Xu K, Wang D. Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine. Metals, 2018, 8(3): 197.(paper)

5. Luo Q, Sun Y, et al. Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification. IEEE Transactions on Instrumentation and Measurement, 2018.(paper)

6. Zhou S, Wu S, Liu H, et al. Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet[J]. Applied Sciences, 2018, 8(9): 1628.(paper)

7. Chu M, Liu X, Gong R, et al. Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere[J]. Journal of Iron and Steel Research International, 2018, 25(7): 706-716.(paper)

8. Ren, R., Hung, T., & Tan, K. C. A generic deep-learning-based approach for automated surface inspection[J]. IEEE Transactions on Cybernetics, 2018, 48(3), 929-940.(paper)

9. Tao, X., Zhang, D., Ma, W., Liu, X., & Xu, D. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks[J]. Applied Sciences, 2018, 8(9): 1575.(paper)

10. Sun, X., Gu, J., Tang, S., & Li, J. Research Progress of Visual Inspection Technology of Steel Products—A Review[J]. Applied Sciences, 2018, 8(11): 2195.(paper)

                                                                                   2017                                                                                                               
1Yongjie Zhao, Yunhui Yan, and Kechen Song. Vision-based automatic detection of steel surface defects in the cold rolling process: considering the influence of industrial liquids and surface textures. The International Journal of Advanced Manufacturing Technology, 2017, 90(5): 1665-1678 .(paper) (website)

2. Xi J, Shentu L, Hu J, et al. Automated surface inspection for steel products using computer vision approach. Applied Optics, 2017.(paper)

3. Zhou S, Chen Y, Zhang D, et al. Classification of surface defects on steel sheet using convolutional neural networks. Materiali in Tehnologije, 2017.(paper)

4. Xin Wen,  Kechen Song, Menghui Niu, Zhipeng Dong and Yunhui Yan. A three-dimensional inspection system for high temperature steel product surface sample height using stereo vision and blue encoded patterns [J]. Optik - International Journal for Light and Electron Optics, 2017, 130: 131-148.  (paper)

5.Ma Y, Li Q, Zhou Y, et al. A surface defects inspection method based on multidirectional gray-level fluctuation. International Journal of Advanced Robotic Systems, 2017.(paper)

6.Xiao M, Jiang M, Li G, et al. An evolutionary classifier for steel surface defects with small sample set. Eurasip Journal on Image & Video Processing, 2017.(paper)

7.Yun J, Kim D, Kim K, et al. Vision-based surface defect inspection for thick steel plates. Optical Engineering, 2017.(paper)

8.Choi D C, Jeon Y J, Kim S H, et al. Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features. ISIJ International, 2017.(paper)

9.Chu M, Zhao J, Liu X, et al. Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres. Chemometrics & Intelligent Laboratory Systems, 2017.(paper)

10.Liu K, Wang H, Chen H, et al. Steel Surface Defect Detection Using a New Haar-Weibull-Variance Model in Unsupervised Manner. IEEE Transactions on Instrumentation & Measurement, 2017.(paper)

11.Chu M, Gong R, Gao S, et al. Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine. Chemometrics & Intelligent Laboratory Systems, 2017.(paper)

                                                                                   2016                                                                                                               
1. Hu H, Liu Y, Liu M, et al. Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm. Neurocomputing, 2016, 181:86-95.(paper)

2. Liu M, Liu Y, Hu H, et al. Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination, Journal of Visual Communication & Image Representation, 2016, 37:70-77.(paper)

3. Luo Q, He Y. A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. Robotics and Computer-Integrated Manufacturing, 2016, 38:16-30.(paper)

4. Jeon, Y. J., Choi, D. C., Lee, S. J., Yun, J. P., & Kim, S. W. Steel-surface defect detection using a switching-lighting
scheme. Applied Optics, 2016, 55(1), 47-57.(paper)

5. Yi L, Li G, Jiang M. An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks. Steel Research International, 2016 .(paper)

6. Gong R, Wu C, Chu M, et al. The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights. Steel Research International, 2016 .(paper)

7. Bulnes F G, García D F, et al. A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces. Journal of Nondestructive Evaluation, 2016 .(paper)

8. Zhou S, Chen Y, Zhang D, et al. Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet. ISIJ International, 2016.(paper)

                                                                                   2015                                                                                                               
1.Xu K, Liu S, Ai Y. Application of Shearlet transform to classification of surface defects for metals. Image & Vision Computing, 2015, 35:23-30.(paper)

2.Jeon Y J, Choi D C, Yun J P, et al. Detection of periodic defects using dual-light switching lighting method on the
surface of thick plates. ISIJ International, 2015, 55(9):1942-1949.(paper)

3.Guan S. Strip steel defect detection based on saliency map construction using gaussian pyramid decomposition. ISIJ International, 2015, 55(9):1950-1955.(paper)

4.Chu M, Gong R. Invariant feature extraction method based on smoothed local binary pattern for strip steel surface defect. ISIJ International, 2015, 55(9):1956-1962.(paper)

5.Gong R, Chu M, Wang A, et al. A fast detection method for region of defect on strip steel surface. ISIJ International, 2015, 55(1):207-212.(paper)

6.Yuan X C, Wu L S, Peng Q. An improved Otsu method using the weighted object variance for defect detection. Applied
Surface Science
, 2015, 349:472-484.(paper)

7.Kwon B K, Won J S, Kang D J. Fast defect detection for various types of surfaces using random forest with VOV features. International Journal of Precision Engineering & Manufacturing, 2015, 16(5):965-970.(paper)

8.Sergio S R, Enrique B, Rodríguez-Juan C P. Unsupervised Classification of Surface Defects in Wire Rod Production Obtained by Eddy Current Sensors. Sensors, 2015, 15(5):10100-10117.(paper)

                                                                                   2014                                                                                                               
1.Neogi N, Mohanta D K, Dutta P K. Review of vision-based steel surface inspection systems. Eurasip Journal on Image &
Video Processing
, 2014, (1):1-19.(paper)

2.Zhao, L., Ouyang, Q., et al. Defect detection in slab surface: A novel dual charge-coupled device imaging-based fuzzy
connectedness strategy. Review of Scientific Instruments, 2014, 85(11):115004.(paper)

3.Zhang, X. L., Ouyang, Q., Peng, S., & Zhao, L. M. Continuous casting slab surface crack depth measurement using sinusoidal phase grating method. Ironmaking and Steelmaking, 2014, 41(5), 387-393.(paper)

4.Sang-Gyu Ryu, Doo-chul Choi, Yong-Ju Jeon, et al. Detection of Scarfing Faults on the Edges of Slabs. ISIJ International, 2014, 54(1): 112-118.(paper)

5.Chu M, Gong R, Wang A. Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine. ISIJ International, 2014, 54(1): 119-124.(paper)

6.Chu M, Wang A, Gong R, et al. Strip Steel Surface Defect Recognition Based on Novel Feature Extraction and Enhanced Least Squares Twin Support Vector Machine. ISIJ International, 2014, 54(7):1638-1645.(paper)

7.Kechen Song, Shaopeng Hu, Yunhui Yan and Jun Li. Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference. ISIJ International, 2014, 54(11):2598-2607.(paper)

8.Mao-xiang, An-na, WANG, et al. Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects. Journal of Iron & Steel Research International, 2014, 21(2):174-180.(paper)

9.Hu H, Li Y, Liu M, et al. Classification of defects in steel strip surface based on multiclass support vector machine. Multimedia Tools & Applications, 2014, 69(1):199-216.(paper)

10.Yun J P, Choi D C, Jeon Y J, et al. Defect inspection system for steel wire rods produced by hot rolling process. International Journal of Advanced Manufacturing Technology, 2014, 70(9-12):1625-1634.(paper)

11.Kechen Song, Shaopeng Hu and Yunhui Yan. Automatic Recognition of Surface Defects on Hot-Rolled Steel Strip Using Scattering Convolution Network. Journal of Computational Information Systems, 2014, 10(7):3049-3055.(paper)

12.Liu, Weiwei, and Yunhui Yan. Automated surface defect detection for cold-rolled steel strip based on wavelet anisotropic
diffusion method. International Journal of Industrial and Systems Engineering, 2014, 17(2):224-239.(paper)

13.Agarwal K, Shivpuri R. Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection. Journal of Intelligent Manufacturing, 2014, 25(6):1289-1299.(paper)

14.Chang, M., Chou, Y. C., Lin, P. T., & Gabayno, J. L. Fast and High-Resolution Optical Inspection System for In-Line Detection and Labeling of Surface Defects. CMC: Computers, Materials & Continua, 2014, 42(2), 125-140.(paper)

15.Jeon, Y. J., Choi, D. C., Lee, S. J., Yun, J. P., & Kim, S. W. Defect detection for corner cracks in steel billets using a wavelet reconstruction method. Journal of the Optical Society of America A, 2014, 31(2), 227-237.(paper)

16.Choi, D. C., Jeon, Y. J., Lee, S. J., Yun, J. P., & Kim, S. W. Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method. Applied optics, 2014, 53(22), 4865-4872.(paper)

                                                                                  2013                                                                                                                
1.Song K, Yan Y. Micro Surface defect detection method for silicon steel strip based on saliency convex active contour
model. Mathematical Problems in Engineering, 2013: 429094.(paper) (website)

2.Song K, Yan Y. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 2013, 285(21):858-864.(paper)

3.S. Ghorai, A. Mukherjee, M. Gangadaran, P.K. Dutta, Automatic defect detection on hot-rolled flat steel products. IEEE
Transactions on Instrumentation and Measurement
, 2013, 62(3): 612-621.(paper)

4.K. Xu, Y.H. Ai, X.Y. Wu, Application of multi-scale feature extraction to surface defect classification of hot-
rolled steels. International Journal of Minerals, Metallurgy, and Materials, 2013, 20(1):37-41.(paper)

5.Yong-Hao AI, Ke XU. Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections. Journal of Iron & Steel Research International, 2013, 20(5):80-86.(paper)

6.Kang D, Yu J J, Won S. Development of an inspection system for planar steel surface using multispectral photometric stereo. Optical Engineering, 2013, 52(3):254-260.(paper)

7.Li W B, Lu C H, Zhang J C. A lower envelope Weber contrast detection algorithm for steel bar surface pit defects. Optics & Laser Technology, 2013, 45(2):654-659.(paper)

8.Ricci M, Ficola A, Fravolini M L, et al. Machine vision and magnetic imaging NDT for the on-line inspection of stainless steel strips. Measurement Science & Technology, 2013, 24(2):025401.(paper)

9.Zhang X, Li W, Xi J, et al. Surface Defect Target Identification on Copper Strip Based on Adaptive Genetic Algorithm and Feature Saliency. Mathematical Problems in Engineering, 2013, 27(2):1-8.(paper)

                                                                                  2012                                                                                                                
1.Landstrom A, Thurley M J. Morphology-Based Crack Detection for Steel Slabs. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(7):866-875.(paper)

2.Li W B, Lu C H, Zhang J C. A local annular contrast based real-time inspection algorithm for steel bar surface defects. Applied Surface Science, 2012, 258(16):6080-6086.(paper)

                                                                                  2011                                                                                                                
1.Agarwal K, Shivpuri R, Zhu Y, et al. Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling. Expert System with Applications, 2011, 38(6):7251-7262.(paper)

2.Zhang X W, Ding Y Q, Lv Y Y, et al. A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert System with Applications, 2011, 38(5):5930-5939.(paper)

3.Ouyang, Q., Zhao, L. M., Ma, F. J., & Zhang, L. Z. Experimental study of surface defects in continuous casting using developed laser scanning system. Ironmaking and Steelmaking, 2011, 38(1), 12-16.(paper)

4.Ouyang, Q., Zhang, L. Z., Zhao, L. M., Zhang, X. L., & Chen, D. F. Experimental study on quantitative surface defect depth detection based on laser scanning technology in continuous casting. Ironmaking and Steelmaking, 2011, 38(5), 363-368.(paper)

5.Zhao, L. M., Ouyang, Q., Chen, D. F., & Wen, L. Y. Surface defects inspection method in hot slab continuous casting process. Ironmaking and Steelmaking, 2011, 38(6), 464-470.(paper)

6.Medina R, Gayubo F, González-Rodrigo L M, et al. Automated visual classification of frequent defects in flat steel coils. International Journal of Advanced Manufacturing Technology, 2011, 57(9-12): 1087-1098.(paper)

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