Open Access
| Issue |
E3S Web Conf.
Volume 603, 2025
International Symposium on Green and Sustainable Technology (ISGST 2024)
|
|
|---|---|---|
| Article Number | 04014 | |
| Number of page(s) | 6 | |
| Section | Sustainable Development | |
| DOI | https://doi.org/10.1051/e3sconf/202560304014 | |
| Published online | 15 January 2025 | |
- M. Zhou, B. Li, J. Wang, K. Fu, A lightweight object detection framework for underwater imagery with joint image restoration and color transformation. J. King Saud Univ. - Comput. Inf. Sci. 35, 101749 (2023). https://doi.org/10.1016/j.jksuci.2023.101749 [Google Scholar]
- G. Xin, H. Xie, S. Kang, Design and development of an intelligent connected access integrated training and assessment platform. J. Electr. Syst. 20, 900–908 (2024). https://doi.org/10.52783/jes.1250 [Google Scholar]
- G. Xin, H. Xie, S. Kang, Y. Chen, Y. Jiang, Improved research on coral bleaching detection model based on FCOS model. Mar. Environ. Res. 200, 106644 (2024). https://doi.Org/10.1016/j.marenvres.2024.106644 [CrossRef] [Google Scholar]
- L. Zeng, B. Sun, D. Zhu, Underwater target detection based on Faster R-CNN and adversarial occlusion network. Eng. Appl. Artif. Intell. 100, 104190 (2021). https://doi.Org/10.1016/j.engappai.2021.104190 [CrossRef] [Google Scholar]
- N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko, End-to-end object detection with transformers, in Proceedings of Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28 (2020), 213. https://doi.org/10.1007/978-3-030-58452-813 [Google Scholar]
- X. Zhu, W. Su, L. Lu, B. Li, X. Wang, J. Dai, Deformable DETR: deformable transformers for end-to-end object detection, in Proceedings of 9th International Conference on Learning Representations, Vienna, Austria, May 3-7 (2021), 1. https://doi.org/10.48550/arXiv.2010.04159 [Google Scholar]
- S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I.S. Kweon, S. Xie, ConvNeXt V2: Codesigning and scaling ConvNets with masked autoencoders, in Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, June 17-24 (2023), 16133. https://doi.org/10.48550/arXiv.2301.00808 [CrossRef] [Google Scholar]
- Y. Wu, K. He, Group normalization, in Proceedings of the European conference on computer vision (ECCV 2018), Munich, Germany, September 8-14 (2018), 3. https://doi.org/10.48550/arXiv.1803.08494 [CrossRef] [Google Scholar]
- K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, June 27-30 (2016), 770. https://doi.org/10.1109/CVPR.2016.90 [CrossRef] [Google Scholar]
- F. Chollet, Xception, Deep learning with depthwise separable convolutions, in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, July 21-26 (2017), 1251. https://doi.org/10.48550/arXiv.1610.02357 [Google Scholar]
- B. Li, Y. Liu, X. Wang, Gradient harmonized single-stage detector, in Proceedings of the AAAI conference on artificial intelligence, Honolulu, USA, January 27-February 1 (2019), 8577. https://doi.org/10.1609/aaai.v33i01.33018577 [CrossRef] [Google Scholar]
- T.Y. Lin, P. Goyal, R. Girshick, K. Hef, P. Dollar, Focal loss for dense object detection, in Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, October 22-29 (2017), 2980. https://doi.org/10.48550/arXiv.1708.02002 [Google Scholar]
- C. Liu, H. Li, S. Wang, M. Zhu, D. Wang, X. Fan, Z. Wang, A dataset and benchmark of underwater object detection for robot picking, in Proceedings of 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shenzhen, China, July 5-9 (2021), 1. https://doi.org/10.1109/ICMEW53276.2021.9455997 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.