||The textile company applied AOI to detect a defect on fabric automatically. In their case, AOI failed to overcome this problem because there are differences defect categories. AOI has defect with categories among others; fold, black thread, broken thread, hooked thread, needle mark, mosquito, stain, thread-off, white spot and uneven thickness. Whereas, Textile company has different defects categories, its categories among others; Thinning, missing weft, stop mark, weft mark, loose weft, warp break, mechanical section and fold back. These differences caused more than 90% of captured images are not defect (overkill). A Professional Inspector in Textile Company reclassified those overkill images into real defect and non-defect images. Our goal in this work is, to reduce a Professional Inspector working loads by proposed two approaches. The first approach is, building a tiny and light CNN architecture as classifier model. While second approach is, combining Autoencoder to reconstruct the dataset as an input to CNN model that built in first approach. The result shows that the models are able to reduce a Professional Inspector working loads up to 90% with maximum FNR 5% and FPR less than 5%.|
|| Abdel Salam Malek, “Online fabric inspection by image processing technology,” Other. Universite de Haute Alsace - Mulhouse, 2012. English. ffNNT : 2012MULH4090ff. fftel-00720041ff|
 Bahera, BK. Mni, MP, “Characterization and classification of fabric defects using discrete cosine transformation and artificial neural network” in Indian Journal of Fibre & Textile Research (IJFTR), vol. 34, no. 4, pp. 421-426, Dec. 2007
 H. Xie, Y. Zhang and Z. Wu, "Fabric Defect Detection Method Combing Image Pyramid and Direction Template," in IEEE Access, vol. 7, pp. 182320-182334, 2019, doi: 10.1109/ACCESS.2019.2959880.
 J. Masci, U. Meier, D. Cire?an, and J. Schmidhuber, “Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction,” Lecture Notes in Computer Science Artificial Neural Networks and Machine Learning – ICANN 2011, pp. 52–59, 2011.
 Jia-Jyun Syue and Hsien-Huang Wu, "Defect Detection of Golf Ball Surface Based on Deep Learning," 2019 CVGIP Computer Vision Graphics and Image Processing Workshop, Taitung, Taiwan, 2019
 Keras Community and Governance, Keras Documentation, [Online]. Available: https://keras.io/
 L. Weninger, M. Kopaczka and D. Merhof, "Defect detection in plain weave fabrics by yarn tracking and fully convolutional networks," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, 2018, pp. 1-6.
 Liu, Z., Zhang, C., Li, C., Ding, S., Dong, Y., & Huang, Y. (2019). Fabric defect recognition using optimized neural networks. Journal of Engineered Fibers and Fabrics. https://doi.org/10.1177/1558925019897396
 M. Polic, I. Krajacic, N. Lepora and M. Orsag, "Convolutional Autoencoder for Feature Extraction in Tactile Sensing," in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3671-3678, Oct. 2019, doi: 10.1109/LRA.2019.2927950.
 Mei, S.; Wang, Y.; Wen, G., “Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model”, Sensors 2018, 18, 1064.
 Q. V. Le, “A tutorial on deep learning part 2: Autoencoders convolutional neural networks and recurrent neural networks”, 2015, [online] Available: https://cs.stanford.edu/~quocle/tutorial2.pdf
 Qinxue Meng, Daniel Catchpoole, David Skillicorn, and Paul J Kennedy. “Relational autoencoder for feature extraction,” arXiv preprint arXiv:1802.03145, 2018.
 S. Shirmohammadi and A. Ferrero, "Camera as the instrument: The rising trend of vision based measurement", IEEE Instrum. Meas. Mag., vol. 17, no. 3, pp. 41-47, Jun. 2014
 Tao, X.; Zhang, D.; Ma, W.; Liu, X.; Xu, D. “Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks,” Appl. Sci. 2018, 8, 1575.
 W. Wang, S. Chen, L. Chen and W. Chang, "A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards," in IEEE Access, vol. 5, pp. 10817-10833, 2017, doi: 10.1109/ACCESS.2016.2631658.
 Y. Zhang, “A better autoencoder for image: Convolutional autoencoder,” in ICONIP17-DCEC, 2018. [Online]. Available: http://users.cecs.anu.edu. au/Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58.pdf, Accessed on Mar. 23 2017
 Yamashita, R., Nishio, M., Do, R.K.G. et al. “Convolutional neural networks: an overview and application in radiology,” Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9