1 |
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.
|
2 |
Lin, T. Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
|
3 |
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
|
4 |
Kim, Y., Kim, J., & Kang, S. (2019). A study on welding deformation prediction for ship blocks using the equivalent strain method based on inherent strain. Applied Sciences, 9(22), 4906.
|
5 |
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159.
|
6 |
Datal, N. (2005). Histograms of oriented gradients for human detection. In Proc. 2005 International Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 886-893). IEEE Computer Society.
|
7 |
김창현, 유홍연, & 홍성훈. (2007). 용접결함 패턴인식을 위한 신경망 알고리즘 적용. 한국콘텐츠학회논문지, 7(1), 65-72.
DOI
|
8 |
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
|
9 |
Tang, Y. X., Tang, Y. B., Peng, Y., Yan, K., Bagheri, M., Redd, B. A., ... & Summers, R. M. (2020). Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ digital medicine, 3(1), 1-8.
DOI
|
10 |
Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6154-6162).
|
11 |
Ahn, H. (2013). Digital Radiography. Journal of the Korean Society for Nondestructive Testing, 33(1), 80-95.
|
12 |
Eggert, C., Zecha, D., Brehm, S., & Lienhart, R. (2017, June). Improving small object proposals for company logo detection. In Proceedings of the 2017 ACM on international conference on multimedia retrieval (pp. 167-174).
|
13 |
Law, H., & Deng, J. (2018). Cornernet: Detecting objects as paired keypoints. In Proceedings of the European conference on computer vision (ECCV) (pp. 734-750).
|
14 |
Kornblith, S., Shlens, J., & Le, Q. V. (2019). Do better imagenet models transfer better?. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2661-2671).
|
15 |
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.
|
16 |
Sizyakin, R., Voronin, V., Gapon, N., Zelensky, A., & Pizurica, A. (2019, June). Automatic detection of welding defects using the convolutional neural network. In Automated Visual Inspection and Machine Vision III (Vol. 11061, pp. 93-101). SPIE.
|
17 |
Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-NMS--improving object detection with one line of code. In Proceedings of the IEEE international conference on computer vision (pp. 5561-5569).
|
18 |
Vilar, R., Zapata, J., & Ruiz, R. (2009). An automatic system of classification of weld defects in radiographic images. Ndt & E International, 42(5), 467-476.
DOI
|
19 |
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
|
20 |
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
DOI
|
21 |
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
|
22 |
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
|
23 |
Chen, X., Hsieh, C. J., & Gong, B. (2021). When vision transformers outperform ResNets without pre-training or strong data augmentations. arXiv preprint arXiv:2106.01548.
|
24 |
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., ... & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022).
|
25 |
Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.
|
26 |
Yun, G. H., Oh, S. J., & Shin, S. C. (2021). Image Preprocessing Method in Radiographic Inspection for Automatic Detection of Ship Welding Defects. Applied Sciences, 12(1), 123.
|