Open Access
Issue |
MATEC Web Conf.
Volume 380, 2023
4th International Symposium on Mechanics, Structures and Materials Science (MSMS 2023)
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Article Number | 01016 | |
Number of page(s) | 6 | |
DOI | https://doi.org/10.1051/matecconf/202338001016 | |
Published online | 01 May 2023 |
- Girshick R, Donahue J, Darrell T, et al, “Rich feature hierarchies for accurate object detection and semantic segmentation, ” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014, pp. 580-587. [Google Scholar]
- Liu W, Anguelov D, Erhan D, et al, “Ssd: Single shot multibox detector, ” European conference on computer vision. Springer, Cham, 2016, pp.21-37. [Google Scholar]
- Redmon J, Divvala S, Girshick R, 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. [Google Scholar]
- Liao X, Lv S, Li D, et al, “YOLOv4-MN3 for PCB Surface Defect Detection, ” Applied Sciences, 2021, 11(24): 11701. [CrossRef] [Google Scholar]
- Zhang B, Fang S, Li Z, “Research on Surface Defect Detection of Rare-Earth Magnetic Materials Based on Improved SSD, ” Complexity, 2021, doi: 10.1155/2021/4795396. [Google Scholar]
- Wang J, Meng Z H. “Deformable Feature Pyramid Network for Aluminum Profile Surface Defect Detection, ” Journal of Physics: Conference Series. IOP Publishing, 2020, 1544(1): 012074. [CrossRef] [Google Scholar]
- K. Xiang, S. Li, M. Luan, Y. Yang, “Aluminum product surface defect detection method based on improved Faster RCNN, ” Chin. J. Sci. Instrum, 2021, pp. 191-198. [Google Scholar]
- S. Ren, K. He, R. Girshick, J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks, ” Advances in neural information processing systems, 2015, 28, doi:10.48550/arXiv.1506.01497. [Google Scholar]
- T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, S. Belongie, “Feature pyramid networks for object detection, ” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pp. 2117-2125. [Google Scholar]
- J. Dai, H. Qi, Y. Xiong, et al, “Deformable convolutional networks, ” Proceedings of the IEEE international conference on computer vision. 2017, pp. 764-773. [Google Scholar]
- C. Guo, B. Fan, Q. Zhang, S. Xiang, C. Pan, “Augfpn: Improving multi-scale feature learning for object detection, ” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, pp. 12595-12604. [Google Scholar]
- K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition, ” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 770-778. [Google Scholar]
- Z. Cai, V. Nuno, “Cascade r-cnn: Delving into high quality object detection, ” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 6154-6162. [Google Scholar]
- T. Y. Lin, P. Goyal, R. Girshick, K. He, D. Piotr, “Focal loss for dense object detection, ” Proceedings of the IEEE international conference on computer vision. 2017, pp. 2980-2988. [Google Scholar]
- J Redmon, A Farhadi, “Yolov3: An incremental improvement, ” arXiv preprint arXiv:1804.02767, 2018, doi: 10.48550/arXiv.1804.02767. [Google Scholar]
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