Document Type : Original/Review Paper

Authors

Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.

10.22044/jadm.2022.11002.2247

Abstract

In this study, an automatic system based on image processing methods using features based on convolutional neural networks is proposed to detect the degree of possible dipping and buckling on the sandwich panel surface by a colour camera. The proposed method, by receiving an image of the sandwich panel, can detect the dipping and buckling of its surface with acceptable accuracy. After a panel is fully processed by the system, an image output is generated to observe the surface status of the sandwich panel so that the supervisor of the production line can better detect any potential defects at the surface of the produced panels. An accurate solution is also provided to measure the amount of available distortion (depth or height of dipping and buckling) on the sandwich panels without needing expensive and complex equipment and hardware.

Keywords

[1] A. Kumar, "Computer-vision-based fabric defect detection: A survey," IEEE transactions on industrial electronics, Vol. 55, pp. 348-363, 2008.
[2] A. Kumar and G. K. Pang, "Defect detection in textured materials using Gabor filters," IEEE Transactions on industry applications, Vol. 38, pp. 425-440, 2002.
[3] D. A. Wheeler, B. Brykczynski, and R. N. Meeson Jr, Software Inspection: An Industry Best Practice for Defect Detection and Removal: IEEE Computer Society Press, 1996.
[4] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM computing surveys (CSUR), Vol. 41, p. 15, 2009.
[5] P. Navarro, C. Fernández-Isla, P. Alcover, and J. Suardíaz, "Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level," Sensors, Vol. 16, p. 1178, 2016.
[6] D. Micucci, M. Mobilio, P. Napoletano, and F. Tisato, "Falls as anomalies? An experimental evaluation using smartphone accelerometer data," Journal of Ambient Intelligence and Humanized Computing, Vol. 8, pp. 87-99, 2017.
[7] M. W. Berry and M. Castellanos, "Survey of text mining," Computing Reviews, Vol. 45, p. 548, 2004.
[8] S. Boriah, V. Chandola, and V. Kumar, "Similarity measures for categorical data: A comparative evaluation," in Proceedings of the 2008 SIAM international conference on data mining, 2008, pp. 243-254.
[9] V. Chandola, S. Boriah, and V. Kumar, "Understanding categorical similarity measures for outlier detection," Technology Report, University of Minnesota, 2008.
[10] M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, "A review of novelty detection," Signal Processing, Vol. 99, pp. 215-249, 2014.
[11] L. Weiwei, Y. Yunhui, L. Jun, Z. Yao, and S. Hongwei, "Automated on-line fast detection for surface defect of steel strip based on multivariate discriminant function," in 2008 Second International Symposium on Intelligent Information Technology Application, 2008, pp. 493-497.
[12] Z. Chao, Z. Desen, and X. Li, "Textural defect detection based on label co-occurrence matrix," JOURNAL-HUAZHONG UNIVERSITY of SCIENCE and TECHNOLOGY NATURE SCIENCE EDITION, Vol. 34, p. 25, 2006.
[13] P. Caleb and M. Steuer, "Classification of surface defects on hot rolled steel using adaptive learning methods," in KES'2000. Fourth International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No. 00TH8516), 2000, pp. 103-108.
[14] K.-L. Mak, P. Peng, and K. F. C. Yiu, "Fabric defect detection using morphological filters," Image and Vision Computing, Vol. 27, pp. 1585-1592, 2009.
[15] A. Cord, F. Bach, and D. Jeulin, "Texture classification by statistical learning from morphological image processing: application to metallic surfaces," Journal of Microscopy, Vol. 239, pp. 159-166, 2010.
[16] D.-H. Shi, T. Gang, S.-Y. Yang, and Y. Yuan, "Research on segmentation and distribution features of small defects in precision weldments with complex structure," NDT and e International, Vol. 40, pp. 397-404, 2007.
[17] R. Anand and P. Kumar, "Flaw detection in radiographic weldment images using morphological watershed segmentation technique," Ndt and E International, Vol. 42, pp. 2-8, 2009.
[18] S. Zhou, D. Liang, and Y. Wei, "Automatic detection of metal surface defects using multi-angle lighting multivariate image analysis," in 2016 IEEE International Conference on Information and Automation (ICIA), 2016, pp. 1588-1593.
[19] C.-h. Chan and G. K. Pang, "Fabric defect detection by Fourier analysis," IEEE transactions on Industry Applications, Vol. 36, pp. 1267-1276, 2000.
[20] B. Zuo and F. Wang, "Surface cutting defect detection of magnet using Fourier image reconstruction," Computer Engineering and Applications, Vol. 52, pp. 256-260, 2016.
[21] J. L. Raheja, S. Kumar, and A. Chaudhary, "Fabric defect detection based on GLCM and Gabor filter: A comparison," Optik, Vol. 124, pp. 6469-6474, 2013.
[22] D. Choi, Y. J. Jeon, J. P. Yun, S. W. Yun, and S. W. Kim, "An algorithm for detecting seam cracks in steel plates," World Acad Sci Eng Technol, Vol. 6, pp. 1456-1459, 2012.
[23] Y.-J. Jeon, D.-c. Choi, J. P. Yun, C. Park, and S. W. Kim, "Detection of scratch defects on slab surface," in 2011 11th International Conference on Control, Automation and Systems, 2011, pp. 1274-1278.
[24] H. Zheng, B. Jiang, and H. Lu, "An adaptive neural-fuzzy inference system (ANFIS) for detection of bruises on Chinese bayberry (Myrica rubra) based on fractal dimension and RGB intensity color," Journal of food engineering, Vol. 104, pp. 663-667, 2011.
[25] M. Yazdchi, M. Yazdi, and A. G. Mahyari, "Steel surface defect detection using texture segmentation based on multifractal dimension," in 2009 International Conference on Digital Image Processing, 2009, pp. 346-350.
[26] J. Blackledge and D. Dubovitskiy, "A surface inspection machine vision system that includes fractal texture analysis," 2008.
[27] Z. Jiuliang, L. Weiwei, Y. Feng, L. Jun, Z. Yao, and Y. Yunhui, "Research on surface quality evaluation system of steel strip based on computer vision," in 2009 Third International Symposium on Intelligent Information Technology Application, 2009, pp. 32-35.
[28] H. Zheng, L. X. Kong, and S. Nahavandi, "Automatic inspection of metallic surface defects using genetic algorithms," Journal of materials processing technology, Vol. 125, pp. 427-433, 2002.
[29] H. Jia, Y. L. Murphey, J. Shi, and T.-S. Chang, "An intelligent real-time vision system for surface defect detection," in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004, pp. 239-242.
[30] C. Lee, C.-H. Choi, J. Choi, Y. Kim, and S. Choi, "Feature extraction algorithm based on adaptive wavelet packet for surface defect classification," in Proceedings of 3rd IEEE International Conference on Image Processing, 1996, pp. 673-676.
[31] A. Kumar and H. C. Shen, "Texture inspection for defects using neural networks and support vector machines," in Proceedings. International Conference on Image Processing, 2002, pp. III-III.
[32] H. Elbehiery, A. Hefnawy, and M. Elewa, "Surface defects detection for ceramic tiles using image processing and morphological techniques," 2005.
[33] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, Vol. 60, pp. 91-110, 2004.
[34] H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006, pp. 404-417.
[35] L. Ding and A. Goshtasby, "On the Canny edge detector," Pattern Recognition, Vol. 34, pp. 721-725, 2001.
[36] C. Cusano, P. Napoletano, and R. Schettini, "Intensity and color descriptors for texture classification," in Image Processing: Machine Vision Applications VI, 2013, p. 866113.
[37] P. Napoletano, "Hand-crafted vs learned descriptors for color texture classification," in International Workshop on Computational Color Imaging, 2017, pp. 259-271.
[38] A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: an astounding baseline for recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 806-813.
[39] P. Napoletano, "Visual descriptors for content-based retrieval of remote-sensing images," International journal of remote sensing, Vol. 39, pp. 1343-1376, 2018.
[40] S. Bianco, L. Celona, P. Napoletano, and R. Schettini, "On the use of deep learning for blind image quality assessment," Signal, Image and Video Processing, Vol. 12, pp. 355-362, 2018.
[41] C. Cusano, P. Napoletano, and R. Schettini, "Combining multiple features for color texture classification," Journal of Electronic Imaging, Vol. 25, p. 061410, 2016.
[42] A. Bhandare, M. Bhide, P. Gokhale, and R. Chandavarkar, "Applications of convolutional neural networks," International Journal of Computer Science and Information Technologies, Vol. 7, pp. 2206-2215, 2016.
[43] O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, "Convolutional neural networks for speech recognition," IEEE/ACM Transactions on audio, speech, and language processing, Vol. 22, pp. 1533-1545, 2014.
[44] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009, pp. 248-255.
[45] Y. Zhou, H. Nejati, T.-T. Do, N.-M. Cheung, and L. Cheah, "Image-based vehicle analysis using deep neural network: A systematic study," in 2016 IEEE International Conference on Digital Signal Processing (DSP), 2016, pp. 276-280.
[46] J. Yue-Hei Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond short snippets: Deep networks for video classification," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 4694-4702.
[47] S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, Vol. 2, pp. 37-52, 1987.
[48] E. W. Forgy, "Cluster analysis of multivariate data: efficiency versus interpretability of classifications," biometrics, Vol. 21, pp. 768-769, 1965.
[49] Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, 2000.
[50] J. Heikkila and O. Silven, "A four-step camera calibration procedure with implicit image correction," in cvpr, 1997, p. 1106.
[51] K. Homik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, Vol. 4, pp. 251-257, 1991.
[52] Torkzadeh V, Toosizadeh S. Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces. Measurement and Control. 2019 Sep; 52(7-8):804-13.
[53] Fallahzadeh, M., Farokhi, F., Harimi, A., Sabbaghi-Nadooshan, R. (2021). Facial Expression Recognition based on Image Gradient and Deep Convolutional Neural Network. Journal of AI and Data Mining, 9(2), 259-268. doi: 10.22044/jadm.2021.9898.2121
[54] Erfani, S. (2021). Automatic Facial Expression Recognition Method Using Deep Convolutional Neural Network. Journal of AI and Data Mining, 9(2), 153-159. doi: 10.22044/jadm.2020.8801.2018