참고문헌 (34)

1. 10.1109/ICCVW.2017.369 

   

2. arXiv 1706 05587 Rethinking atrous convolution for semantic image segmentation chen 2017 

3. Proc Int Conf Med Image Comput Comput -Assist Intervent U-Net: Convolutional networks for biomedical image segmentation ronneberger 2015 234 

4. 10.1109/ISPA.2019.8868619 

   

5. Tripicchio, Paolo, Camacho-Gonzalez, Gerardo, D’Avella, Salvatore. Welding defect detection: coping with artifacts in the production line. International journal of advanced manufacturing technology, vol.111, no.5, 1659-1669.

   상세보기

6. Zhang, Chuxu, Song, Dongjin, Chen, Yuncong, Feng, Xinyang, Lumezanu, Cristian, Cheng, Wei, Ni, Jingchao, Zong, Bo, Chen, Haifeng, Chawla, Nitesh V.. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. Proceedings of the ... aaai conference on artificial intelligence, vol.33, 1409-1416.

   상세보기

7. arXiv 1508 01991 Bidirectional LSTM-CRF models for sequence tagging huang 2015 

8. Malamas, Elias N, Petrakis, Euripides G.M, Zervakis, Michalis, Petit, Laurent, Legat, Jean-Didier. A survey on industrial vision systems, applications and tools. Image and vision computing, vol.21, no.2, 171-188.

   상세보기

9. Mital, Anil, Govindaraju, M, Subramani, B. A comparison between manual and hybrid methods in parts inspection. Integrated manufacturing systems : IMS, vol.9, no.6, 344-349.

   상세보기

10. 10.1109/IECON.2006.347535 

    

11. Conci, A., ProenU00E7;a, C.B.. A Computer Vision Approach for Textile Inspection. Textile research journal : publication of Textile Research Institute, Inc. and the Textile Foundation, vol.70, no.4, 347-350.

    상세보기

12. Proc Adv Neural Inf Process Syst ImageNet classification with deep convolutional neural networks krizhevsky 2012 1097 

13. 10.1109/ICCV.2017.74 

    

14. Jing Tian, Morillo, Carlos, Azarian, Michael H., Pecht, Michael. Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.63, no.3, 1793-1803.

    상세보기

15. Xue-wu, Z., Yan-qiong, D., Yan-yun, L., Ai-ye, S., Rui-yu, L.. A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert systems with applications, vol.38, no.5, 5930-5939.

    상세보기

16. Sassi, Paolo, Tripicchio, Paolo, Avizzano, Carlo Alberto. A Smart Monitoring System for Automatic Welding Defect Detection. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.66, no.12, 9641-9650.

    상세보기

17. Augustauskas, Rytis, Lipnickas, Arūnas. Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder. Sensors, vol.20, no.9, 2557-.

    상세보기

18. Feng, Yunhe, Chen, Zongyao, Wang, Dali, Chen, Jian, Feng, Zhili. DeepWelding: A Deep Learning Enhanced Approach to GTAW Using Multisource Sensing Images. IEEE transactions on industrial informatics, vol.16, no.1, 465-474.

    상세보기

19. Proc IEEE Region 10 Conf (TENCON) Semiconductor wafer surface: Automatic defect classification with deep CNN phua 2020 714 

20. Proc Adv Neural Inf Process Syst Convolutional LSTM network: A machine learning approach for precipitation nowcasting xingjian 2015 802 

21. arXiv 1412 6980 Adam: A method for stochastic optimization kingma 2014 

22. IEEE Trans Instrum Meas Fabric defect segmentation method based on deep learning huang 2021 10.1109/TIM.2021.3115210 70 1 

    상세보기

23. Yoo, Yong-Ho, Kim, Ue-Hwan, Kim, Jong-Hwan. Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection. IEEE transactions on cybernetics, vol.52, no.6, 4688-4700.

    상세보기

24. Proc Adv Neural Inf Process Syst PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs wang 2017 879 

25. Lin, Hsien-I., Wibowo, Fauzy Satrio. Image Data Assessment Approach for Deep Learning-Based Metal Surface Defect-Detection Systems. IEEE access : practical research, open solutions, vol.9, 47621-47638.

    상세보기

26. 10.1109/WCSP.2017.8171119 

    

27. He, Zhiquan, Liu, Qifan. Deep Regression Neural Network for Industrial Surface Defect Detection. IEEE access : practical research, open solutions, vol.8, 35583-35591.

    상세보기

28. Mirapeix, J., Garcia-Allende, P.B., Cobo, A., Conde, O.M., Lopez-Higuera, J.M.. Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & E international : independent nondestructive testing and evaluation, vol.40, no.4, 315-323.

    상세보기

29. Cha, Young‐Jin, Choi, Wooram, Büyüköztürk, Oral. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks. Computer-aided civil and infrastructure engineering, vol.32, no.5, 361-378.

    상세보기

30. 10.1109/IJCNN.2016.7727522 

    

31. Kang, Gaoqiang, Gao, Shibin, Yu, Long, Zhang, Dongkai. Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning. IEEE transactions on instrumentation and measurement, vol.68, no.8, 2679-2690.

    상세보기

32. Hu, Chuanfei, Wang, Yongxiong. An Efficient Convolutional Neural Network Model Based on Object-Level Attention Mechanism for Casting Defect Detection on Radiography Images. IEEE transactions on industrial electronics : a publication of the IEEE Industrial Electronics Society, vol.67, no.12, 10922-10930.

    상세보기

33. Lee, Jeongick, Um, Kiwoan. A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network. Optics and lasers in engineering, vol.34, no.3, 149-158.

    상세보기

34. Ren, Shaoqing, He, Kaiming, Girshick, Ross, Sun, Jian. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE transactions on pattern analysis and machine intelligence, vol.39, no.6, 1137-1149.

    상세보기

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