The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions

Akkus, 2017, Deep learning for brain MRI segmentation: state of the art and future directions, J. Digit. Imaging, 10.1007/s10278-017-9983-4

Aslam, 2019, 7

Ayan, 2019, Diagnosis of pneumonia from chest X-ray images using deep learning

Bahaghighat, 2019, Vision inspection of Bottle caps in drink factories using convolutional neural networks

Bier, 2019

Chollet, 2017

Erhan, 2010, Why does unsupervised pre-training help deep learning?, J. Mach. Learn. Res., 11

Hansen, 2018, Towards on-farm pig face recognition using convolutional neural networks, Comput. Ind., 98, 10.1016/j.compind.2018.02.016

He, 2016, Deep residual learning for image recognition

Horev, 2018

Howard, 2017

Jang, 2019

Jiang, 2019, Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues, Artif. Intell. Agric., 1

Kakani, 2020, A critical review on computer vision and artificial intelligence in food industry, J. Agric. Food Res., 2

Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25

LeCun, 1995, Convolutional networks for images, speech, and time series, 3361

LeCun, 1990, Handwritten zip code recognition with multilayer networks

LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 10.1109/5.726791

Li, 2018, Deep learning for smart industry: efficient manufacture inspection system with fog computing, IEEE Trans. Industr. Inform., 14

Li, 2020, A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision, Comput. Ind. Eng., 146, 10.1016/j.cie.2020.106530

Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 10.1016/j.media.2017.07.005

Liu, 2019, Deep learning in medical ultrasound analysis: a review, Engineering, 5, 10.1016/j.eng.2018.11.020

Liuzz, 2018, A review on the use of drones for precision agriculture, 275

Lundervold, 2019, An overview of deep learning in medical imaging focusing on MRI, Z. Med. Phys., 29, 10.1016/j.zemedi.2018.11.002

Machine Vision Systems, 2020

Moreno, 2019, Towards automatic crack detection by deep learning and active thermography, International Work-Conference on Artificial Neural Networks, IWANN, Advances in Computational Intelligence

Nasirahmadi, 2019, Deep learning and machine vision approaches for posture detection of individual pigs, Sensors, 19, 10.3390/s19173738

Nasiri, 2020, An automatic sorting system for unwashed eggs using deep learning, J. Food Eng., 283, 10.1016/j.jfoodeng.2020.110036

Nasiria, 2019, Image-based deep learning automated sorting of date fruit, Postharvest Biol. Technol., 153

Palaciosa, 2020, Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions, Comput. Electron. Agric., 178

Ren, 2018, A generic deep-learning-Based approach for automated surface inspection, IEEE Trans. Cybern., 48, 10.1109/TCYB.2017.2668395

Rong, 2019, Computer vision detection of foreign objects in walnuts using deep learning, Comput. Electron. Agric., 162, 10.1016/j.compag.2019.05.019

Ruppel, 2020, NANCY: combining adversarial networks with cycle-consistency for robust multi-modal image registration, Int. J. Comput. Inf. Eng., 14

Simonyan, 2014

Sioma, 2020, Automated control of surface defects on ceramic tiles using 3D image analysis, Materials, 13, 10.3390/ma13051250

Smith, 2005, Dynamic photometric stereo - a new technique for moving surface analysis, Image Vis. Comput., 23, 10.1016/j.imavis.2005.01.007

Smith, 2019, Weed classification in grasslands using convolutional neural networks, Applications of Machine Learning, SPIE Optics Photonics, 10.1117/12.2530092

Steward, 2019

Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 10.1109/TMI.2016.2535302

Tang, 2019, Component recognition method based on deep learning and machine vision, ICIGP’ 19: Proceedings of the 2nd International Conference on Image and Graphics Processing, 10.1145/3313950.3313962

Villalba-Diez, 2019, Deep learning for industrial computer vision quality control in the printing industry 4.0, Sensors, 19, 3987, 10.3390/s19183987

Wang, 2018, Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data, Sensors, 4

Wang, 2019, A review on weed detection using ground-based machine vision and image, Comput. Electron. Agric., 158, 10.1016/j.compag.2019.02.005

Wang, 2019, Machine vision intelligence for product defect inspection based on deep learning and Hough transform, J. Manuf. Syst., 51, 10.1016/j.jmsy.2019.03.002

Williams, 2019, Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms, Biosyst. Eng., 181, 10.1016/j.biosystemseng.2019.03.007

Würschinger, 2020, Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware, Proc. CIRP, 90, 10.1016/j.procir.2020.01.121

Xie, 2020, Detection of Atlantic salmon bone residues using machine vision technology, Food Control

Yang, 2019, The internet of things for smart manufacturing: a review, IISE Trans., 51, 10.1080/24725854.2018.1555383

Zhihong, 2017, A vision-based robotic grasping system using deep learning for garbage sorting