Machine vision intelligence for product defect inspection based on deep learning and Hough transform

Wang, 2018, Deep learning for smart manufacturing: methods and applications, J Manuf Syst, 48, 144, 10.1016/j.jmsy.2018.01.003

Luo, 2019, Digital twin for CNC machine tool: modeling and using strategy, J Ambient Intell Humaniz Comput, 10, 1129, 10.1007/s12652-018-0946-5

Loizou, 2015, Automated wear characterization for broaching tools based on machine vision systems, J Manuf Syst, 37, 558, 10.1016/j.jmsy.2015.04.005

Tootooni, 2016, Online non-contact surface finish measurement in machining using graph theory-based image analysis, J Manuf Syst, 41, 266, 10.1016/j.jmsy.2016.09.007

Fernandez-Robles, 2017, Identification of milling inserts in situ based on a versatile machine vision system, J Manuf Syst, 45, 48, 10.1016/j.jmsy.2017.08.002

Wang, 2018, In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing, J Manuf Syst, 47, 83, 10.1016/j.jmsy.2018.04.003

Schmidt, 2014, Depth camera based collision avoidance via active robot control, J Manuf Syst, 33, 711, 10.1016/j.jmsy.2014.04.004

Kuo, 2017, Automated defect inspection system for CMOS image sensor with micro multi-layer non-spherical lens module, J Manuf Syst, 45, 248, 10.1016/j.jmsy.2017.10.004

Aminzadeh, 2015, Automatic thresholding for defect detection by background histogram mode extents, J Manuf Syst, 37, 83, 10.1016/j.jmsy.2015.09.004

Malamas, 2003, A survey on industrial vision systems, applications and tools, Image Vis Comput, 21, 171, 10.1016/S0262-8856(02)00152-X

Brosnan, 2004, Improving quality inspection of food products by computer vision – a review, J Food Eng, 61, 3, 10.1016/S0260-8774(03)00183-3

Li, 2013, Deep learning: methods and applications, Found. Trends Signal Process., 7, 197

Jason, 2010

Li, 2018, Deep learning for smart industry: efficient manufacture inspection system with fog computing, IEEE Trans Ind Inform, 14, 4665, 10.1109/TII.2018.2842821

Wu, 2017, A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, J Manuf Syst, 43, 25, 10.1016/j.jmsy.2017.02.011

Iglesias, 2018, Automated vision system for quality inspection of slate slabs, Comput Ind, 99, 119, 10.1016/j.compind.2018.03.030

Haddad, 2018, Multifeature, sparse-based approach for defects detection and classification in semiconductor units, IEEE Trans Autom Sci Eng, 15, 145, 10.1109/TASE.2016.2594288

Zeng, 2016, A visual weld edge recognition method based on light and shadow feature construction using directional lighting, J Manuf Process, 24, 19, 10.1016/j.jmapro.2016.07.002

Roberto, 2009

Tsai, 2017, Machine vision-based positioning and inspection using expectation-maximization technique, IEEE Trans Instrum Meas, 66, 2858, 10.1109/TIM.2017.2717284

Kong, 2017, Accurate and efficient inspection of speckle and scratch defects on surfaces of planar products, IEEE Trans Ind Inform, 13, 1855, 10.1109/TII.2017.2668438

Jian, 2017, Automatic surface defect detection for mobile phone screen glass based on machine vision, Appl Soft Comput, 52, 348, 10.1016/j.asoc.2016.10.030

Tao, 2018, Wire defect recognition of spring-wire socket using multitask convolutional neural networks, IEEE Trans Compon Packag Manuf Technol, 8, 689, 10.1109/TCPMT.2018.2794540

Tello, 2018, Deep-structured machine learning model for the recognition of mixed-defect patterns in semiconductor fabrication processes, IEEE Trans Semicond Manuf, 31, 315, 10.1109/TSM.2018.2825482

Yang, 2018, An automatic aperture detection system for LED cup based on machine vision, Multimed Tools Appl, 77, 23227, 10.1007/s11042-018-5639-8

Yuan, 2018, Vision-based defect detection for mobile phone cover glass using deep neural networks, Int J Precis Eng Manuf, 19, 801, 10.1007/s12541-018-0096-x

Liu, 2017, Transfer learning on convolutional activation feature as applied to a building quality assessment robot, Int J Adv Robot Syst, 14, 10.1177/1729881417712620

Mei, 2018, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model, Sensors, 18, 10.3390/s18041064

Khosravan, 2018, Semi-supervised multi-task learning for lung cancer diagnosis, 710

Shao, 2013, Feature selection for manufacturing process monitoring using cross-validation, J Manuf Syst, 32, 550, 10.1016/j.jmsy.2013.05.006

Canny, 1986, A computational approach to edge detection, IEEE Trans Pattern Anal Mach Intell, 8, 679, 10.1109/TPAMI.1986.4767851

P.V.C. Hough Method and Means for Recognizing Complex Patterns. US Patents 3069654;1962.

Matas, 2000, Robust detection of lines using the progressive probabilistic hough transform, Comput Vis Image Underst, 78, 119, 10.1006/cviu.1999.0831

Mark, 2018, MobileNetV2: inverted residuals and linear bottlenecks, 4510

Ioffe, 2015, Batch normalization: accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd international conference on machine learning, 37, 448

Sifre, 2014

Srivastava, 2015, Training Very Deep Networks

Pulli, 2012, Real-time computer vision with OpenCV, Commun ACM, 55, 61, 10.1145/2184319.2184337

Abadi, 2016, TensorFlow: a system for large-scale machine learning, 265

Simonyan, 2014, Very deep convolutional networks for large-scale image recognition

Kingma, 2015, Adam: a method for stochastic optimization

Van der Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579