References
- B. Staar, B. Staara, M. Lütjena, and M. Freitag, Anomaly detection with convolutional neural networks for industrial surface inspection, Procedia CIRP 79 (1), 484–489 (2019).
- C. C. Huang, et al., Study on Machine Learning Based Intelligent Defect Detection System. MATEC Web of Conferences. EDP Sciences 01010, 2018.
- S. Ren, et al., Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inform. Process. Syst. 39 (6), 91–99 (2015).
- K. He, et al., Mask r-cnn. Proceedings of the IEEE international conference on computer vision. 2017, pp. 2961–2969.
- J. Redmon, et al., You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 779–788.
- W. Liu, et al., SSD: Single shot multibox detector. European conference on computer vision. Springer, Cham, 2016, pp. 21–37.
- J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pp. 3431–3440.
- H. Noh, S. Hong, and B. Han, Learning deconvolution network for semantic segmentation. Proceedings of the IEEE international conference on computer vision. 2015, pp. 1520–1528.
- V. Badrinarayanan, A. Kendall, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE T. Pattern Anal. 39 (12), 2481–2495 (2017).
- I. Goodfellow, et al., Generative adversarial nets. Adv. Neural Inform. Process. Syst. 27, 2672–2680 (2014).
- M. Mirza and S. Osindero, Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
- A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
- X. Wang and A. Gupta, Generative image modeling using style and structure adversarial networks. European Conference on Computer Vision. Springer, Cham, 2016, pp. 318–335.
- J-Y. Zhu, et al., Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision. 2017, pp. 2223–2232.
- Y. Zhang, A Better Autoencoder for Image: Convolutional Autoencoder. ICONIP17-DCEC. 2017.
- TensorFlow, https://www.tensorflow.org/
- Keras Documentation, https://keras.io/
- Precision and recall, https://en.wikipedia.org/wiki/Precision_and_recall
- O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015, pp. 234–241.
- J. Redmon and A. Farhadi, YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pp. 7263–7271.
- Cognex Corporation, VISIONPRO VIDI, https://www.scognex.com/products/deep-learning/visionpro-vidi (2020).
- Saige-Research, SAIGE-VISION, 2020, http://www.saigesresearch.ai/home/sub.php?menukey=61.
- C. Liu, et al., Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019, pp. 82–92.