Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks

[1]  Stefano Roccella,et al.  Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY , 2020, Sensors.

[2]  Xiang Li,et al.  Deep residual learning-based fault diagnosis method for rotating machinery. , 2019, ISA transactions.

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  M. A. Khan,et al.  Machine vision system: a tool for quality inspection of food and agricultural products , 2012, Journal of Food Science and Technology.

[5]  Romulo Gonçalves Lins,et al.  Architecture for multi-camera vision system for automated measurement of automotive components , 2013, 2013 IEEE International Systems Conference (SysCon).

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Andrew Wilson,et al.  Calibrating cameras in an industrial produce inspection system , 2017, Comput. Electron. Agric..

[8]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  A. S. Prabuwono,et al.  Automated Visual Inspection for Bottle Caps Using Fuzzy Logic , 2019 .

[10]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[11]  Fangfang Li,et al.  A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface , 2020, Sensors.

[12]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[13]  Kincho H. Law,et al.  Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning , 2018, Smart and sustainable manufacturing systems.

[14]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[17]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Wenming Huan,et al.  Review of research on lightweight convolutional neural networks , 2020, 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC).

[19]  Bernd Scholz-Reiter,et al.  Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection , 2016 .

[20]  Xu Li,et al.  Machinery fault diagnosis with imbalanced data using deep generative adversarial networks , 2020 .

[21]  Birgit Kirsch,et al.  Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems , 2019 .

[22]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[23]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[24]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.