An improved automatic defect identification system on natural leather via generative adversarial network

References

• Amorim, W. P., H. Pistori, M. Conti Pereira, and M. Antonio Chagas Jacinto. 2010. “Attributes Reduction Applied to Leather Defects Classification.” In 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, Gramado, Brazil, 353–359. IEEE.
   Google Scholar
• Aslam, M., T. M. Khan, S. Saud Naqvi, G. Holmes, and R. Naffa. 2019. “On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey.” IEEE Access 7: 176065–176086. doi:10.1109/ACCESS.2019.2957427.
   Web of Science ®Google Scholar
• Bong, H.-Q., Q.-B. Truong, H.-C. Nguyen, and M.-T. Nguyen. 2018. “Vision-based Inspection System for Leather Surface Defect Detection and Classification.” In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 300–304. IEEE.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Canny, J. 1986. “A Computational Approach to Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 6: 679–698. doi:10.1109/TPAMI.1986.4767851.
   PubMed Web of Science ®Google Scholar
• Cover, T., and P. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory 13 (1): 21–27. doi:10.1109/TIT.1967.1053964.
   Web of Science ®Google Scholar
• Faiz, F. A., and A. Azhari. 2020. “Tanned and Synthetic Leather Classification Based on Images Texture with Convolutional Neural Network.” Knowledge Engineering and Data Science 3 (2): 77–88. doi:10.17977/um018v3i22020p77-88.
   Google Scholar
• Farnia, P., M. Mohammadi, E. Najafzadeh, M. Alimohamadi, B. Makkiabadi, and A. Ahmadian. 2020. “High-quality Photoacoustic Image Reconstruction Based on Deep Convolutional Neural Network: Towards Intra-operative Photoacoustic Imaging.” Biomedical Physics & Engineering Express 6 (4): 45019. doi:10.1088/2057-1976/ab9a10.
   Web of Science ®Google Scholar
• Fukushima, K., and S. Miyake. 1982. “Neocognitron: A Self-organizing Neural Network Model for A Mechanism of Visual Pattern Recognition.” In Competition and Cooperation in Neural Nets, 267–285. Berlin, Heidelberg: Springer.
   Google Scholar
• Gan, Y. S., S.-S. Chee, Y.-C. Huang, S.-T. Liong, and W.-C. Yau. 2020. “Automated Leather Defect Inspection Using Statistical Approach on Image Intensity.” Journal of Ambient Intelligence and Humanized Computing 12 (10): 9269–9285.
   Web of Science ®Google Scholar
• Gan, Y. S., S.-T. Liong, D. Zheng, Y. Xia, W. Shuli, M. Lin, and Y.-C. Huang. 2021. “Detection and Localization of Defects on Natural Leather Surfaces.” Journal of Ambient Intelligence and Humanized Computing 1–15.
   Web of Science ®Google Scholar
• Goodfellow, I. J., J. Pouget-Abadie, M. Mirza, X. Bing, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative adversarial nets.” Advances in neural information processing systems 27 .
   Google Scholar
• Hashim, S. A. M., N. Jamaluddin, and A. Hasbullah. 2017. “Automatic Classification of Animal Skin for Leather Products Using Backpropagation Neural Network.” In 4th National Conference On Research And Education.
   Google Scholar
• Haykin, S. 1994. Neural Networks: A Comprehensive Foundation. New York: MacMillan College Publishing Co.
   Google Scholar
• Jain, M., C. Meegan, and S. Dev. 2021. “Using GANs to Augment Data for Cloud Image Segmentation Task.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 3452-3455). IEEE .
   Google Scholar
• Jawahar, M., N. K. Chandra Babu, K. L. J. A. Vani, L. Jani Anbarasi, and S. Geetha. 2021b. “Vision Based Inspection System for Leather Surface Defect Detection Using Fast Convergence Particle Swarm Optimization Ensemble Classifier Approach.” Multimedia Tools and Applications 80 (3): 4203–4235. doi:10.1007/s11042-020-09727-3.
   Web of Science ®Google Scholar
• Jawahar, M., L. Jani Anbarasi, S. Graceline Jasmine, R. V. Modigari Narendra, and V. Karthik. 2021a. “A Machine Learning-Based Multi-feature Extraction Method for Leather Defect Classification.” In Inventive Computation and Information Technologies, 189–202. Singapore: Springer.
   Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
   Google Scholar
• Kanopoulos, N., N. Vasanthavada, and R. L. Baker. 1988. “Design of an Image Edge Detection Filter Using the Sobel Operator.” IEEE Journal of Solid-state Circuits 23 (2): 358–367. doi:10.1109/4.996.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 25: 1097–1105.
   Google Scholar
• Kwak, C., J. A. Ventura, and K. Tofang-Sazi. 2000. “A Neural Network Approach for Defect Identification and Classification on Leather Fabric.” Journal of Intelligent Manufacturing 11 (5): 485–499. doi:10.1023/A:1008974314490.
   Web of Science ®Google Scholar
• Lewis, D. D. 1998. “Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval.” In European Conference on Machine Learning, 4–15. Berlin, Heidelberg: Springer.
   Google Scholar
• Liong, S.-T., Y. Siang Gan, Y.-C. Huang, K.-H. Liu, and W.-C. Yau. 2019a. “Integrated Neural Network and Machine Vision Approach for Leather Defect Classification.” arXiv preprint arXiv 1905.11731v1.
   Google Scholar
• Liong, S.-T., Y. Siang Gan, Y.-C. Huang, C.-A. Yuan, and H.-C. Chang. 2019b. “Automatic Defect Segmentation on Leather with Deep Learning.” arXiv preprint arXiv 1903.12139v1.
   Google Scholar
• Liong, S.-T., Y. Siang Gan, D. Zheng, H.-X. Xua, H.-Z. Zhang, R.-K. Lyu, K.-H. Liu, et al. 2019c. “Evaluation of the Spatio-temporal Features and Gan for Micro-expression Recognition System.” Journal of Signal Processing Systems 92 (7): 705–725.
   Web of Science ®Google Scholar
• Liong, S.-T., D. Zheng, Y.-C. Huang, and Y. Siang Gan. 2020. “Leather Defect Classification and Segmentation Using Deep Learning Architecture.” International Journal of Computer Integrated Manufacturing 33 (10–11): 1105–1117. doi:10.1080/0951192X.2020.1795928.
   Web of Science ®Google Scholar
• Liu, K.-H., Q.-S. Jin, X. Huang-Chao, Y.-S. Gan, and S.-T. Liong. 2021. “Micro-expression Recognition Using Advanced Genetic Algorithm.” Signal Processing: Image Communication 93: 116153.
   Web of Science ®Google Scholar
• Lloyd, S. 1982. “Least Squares Quantization in PCM.” IEEE Transactions on Information Theory 28 (2): 129–137. doi:10.1109/TIT.1982.1056489.
   Web of Science ®Google Scholar
• Marr, D., and E. Hildreth. 1980. “Theory of Edge Detection.” Proceedings of the Royal Society of London. Series B. Biological Sciences 207 (1167): 187–217.
   PubMed Web of Science ®Google Scholar
• Otsu, N. 1979. “A Threshold Selection Method from Gray-level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics 9 (1): 62–66. doi:10.1109/TSMC.1979.4310076.
   Web of Science ®Google Scholar
• Papavasileiou, A., P. Aivaliotis, S. Aivaliotis, and S. Makris. 2021. “An Optical System for Identifying and Classifying Defects of Metal Parts.” International Journal of Computer Integrated Manufacturing 35 (3): 326–340.
   Web of Science ®Google Scholar
• Pazzaglia, G., M. Martini, R. Rosati, L. Romeo, and E. Frontoni. 2020. “A Deep Learning-Based Approach for Automatic Leather Classification in Industry 4.0.” In ICPR Workshops ( 4), 662–674.
   Google Scholar
• Pereira, R. F., C. M. S. Medeiros, and P. P. Rebouças Filho. 2018. “Goat Leather Quality Classification Using Computer Vision and Machine Learning.” In 2018 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE.
   Google Scholar
• Prewitt, J. M. S. 1970. “Object Enhancement and Extraction.” Picture Processing and Psychopictorics 10 (1): 15–19.
   Google Scholar
• Quinlan, J. R. 1986. “Induction of Decision Trees.” Machine Learning 1 (1): 81–106. doi:10.1007/BF00116251.
   Google Scholar
• Rawashdeh, N. A., J. M. Abu-Khalaf, W. Khraisat, and S. S. Al-Hourani. 2018. “A Visual Inspection System of Glass Ampoule Packaging Defects: Effect of Lighting Configurations.” International Journal of Computer Integrated Manufacturing 31 (9): 848–856. doi:10.1080/0951192X.2018.1447145.
   Web of Science ®Google Scholar
• Roberts, L. G. 1963. “Machine perception of three-dimensional solids.” PhD diss., Massachusetts Institute of Technology.
   Google Scholar
• Russakovsky, O., J. Deng, S. Hao, J. Krause, S. Satheesh, M. Sean, Z. Huang, et al. 2015. “Imagenet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–252. DOI:10.1007/s11263-015-0816-y.
   Web of Science ®Google Scholar
• Saqlain, M., Q. Abbas, and J. Yun Lee. 2020. “A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes.” IEEE Transactions on Semiconductor Manufacturing 33 (3): 436–444. doi:10.1109/TSM.2020.2994357.
   Web of Science ®Google Scholar
• Saxena, A., A. Khanna, and D. Gupta. 2020. “Emotion Recognition and Detection Methods: A Comprehensive Survey.” Journal of Artificial Intelligence and Systems 2 (1): 53–79. doi:10.33969/AIS.2020.21005.
   Google Scholar
• Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large- Scale Image Recognition.” arXiv preprint arXiv 1409.1556v6.
   Google Scholar
• Sun, P., N. Aljeri, and A. Boukerche. 2020. “Machine Learning-based Models for Real-time Traffic Flow Prediction in Vehicular Networks.” IEEE Network 34 (3): 178–185. doi:10.1109/MNET.011.1900338.
   Web of Science ®Google Scholar
• Suykens, J. A. K., and J. Vandewalle. 1999. “Least Squares Support Vector Machine Classifiers.” Neural Processing Letters 9 (3): 293–300. doi:10.1023/A:1018628609742.
   Web of Science ®Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. “Going Deeper with Convolutions.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9.
   Google Scholar
• Viana, R., R. B. Rodrigues, M. A. Alvarez, and H. Pistori. 2007. “SVM with Stochastic Parameter Selection for Bovine Leather Defect Classification.” In Pacific-Rim Symposium on Image and Video Technology, 600–612. Santiago de Chile, Chile: Springer.
   Google Scholar
• Villar, P., M. Mora, and P. Gonzalez. 2011. “A New Approach for Wet Blue Leather Defect Segmentation.” In Iberoamerican Congress on Pattern R E Co Gnition, 591–598. Berlin, Heidelberg: Springer.
   Google Scholar
• Wei, X., D. Wei, D. Suo, L. Jia, and L. Yujie. 2020. “Multi-target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model.” IEEE Access 8: 61973–61988. doi:10.1109/ACCESS.2020.2984264.
   Web of Science ®Google Scholar
• Winiarti, S., A. Prahara, D. P. I. Murinto, and P. Ismi. 2018. “Pretrained Convolutional Neural Network for Classification of Tanning Leather Image.” International Journal of Advanced Computer Science and Applications 9 (1). doi:10.14569/IJACSA.2018.090129.
   Web of Science ®Google Scholar
• Zhou, Y., M. Zhuxi, X. Shi, and K. Zhang. 2019. “An Adaptive Clustering Method Detecting the Surface Defects on Linear Guide Rails.” International Journal of Computer Integrated Manufacturing 32 (8): 798–808. doi:10.1080/0951192X.2019.1636409.
   Web of Science ®Google Scholar