Side-Scan Sonar Image Detection of Shipwrecks Based on CSC-YOLO Algorithm
Source abstract
Abstract
Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Maritime Navigation and Safety · Maritime and Coastal Archaeology · Marine and Coastal Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
Show source-provided references
1. Development status of marine surveying and mapping technology
2. From acoustics to underwater archaeology: Deep investigation of a shallow lake using high-resolution hydroacoustics-The case of Lake Lednica10.1111/arcm.12663 ↗
3. Archaeologic machine learning for shipwreck detection using lidar and sonar10.3390/rs13091759 ↗
4. Novel wreck salvaging method using curved rectangular pipe basing method: A case study of “Yangtze River Estuary II” ancient shipwreck salvage project10.1016/j.undsp.2023.11.016 ↗
5. Rescue of inland waterway shipwrecks
6. A comprehensive buried shipwreck detection method based on 3-D SBP data10.1109/JOE.2023.3318793 ↗
7. Environmental and economic loss analyses of the oil discharge from shipwreck for salvage planning10.1016/j.marpolbul.2020.111142 ↗
8. Study on the classification performance of underwater sonar image classification based on convolutional neural networks for detecting a submerged human body
9. AUV-based side-scan sonar real-time method for underwater-object detection10.3390/jmse11040690 ↗
10. Sonar image intelligent processing in seabed pipeline detection: Review and application10.1088/1361-6501/ad1919 ↗
11. Artificial reef detection method for multibeam sonar imagery based on convolutional neural networks10.3390/rs14184610 ↗
12. A study on seafloor substrate classification based on texture features of side-scan sonar images
13. CFAR detection algorithm for objects in sonar images
14. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014, arXiv:1311.2524.10.1109/CVPR.2014.81 ↗
15. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” 2016, arXiv:1506.01497.10.1109/TPAMI.2016.2577031 ↗
16. N. Carion, F. Massa, and G. Synnaeve, “End-to-end object detection with transformers,” 2020, arXiv:2005.12872.10.1007/978-3-030-58452-8_13 ↗
17. J. Redmon, S. Divvala, and R. Girshick, “You only look once: Unified, real-time object detection,” 2016, arXiv:1506.02640.10.1109/CVPR.2016.91 ↗
18. Underwater object classification in synthetic aperture sonar imagery using deep convolutional neural networks
19. The application of convolutional neural networks for automatic detection of underwater object in side scan sonar images
20. Convolutional neural network-based real-time ROV detection using forward-looking sonar image
21. Sonar image object detection and recognition based on convolution neural network
22. Lightweight DETR-YOLO method for detecting shipwreck object in side-scan sonar
23. Detection of small objects in side-scan sonar images using an enhanced YOLOv7-based approach
24. Underwater small object detection in side-scan sonar images based on improved YOLOv5
25. S. Woo, J. Park, J. Lee, and I. Kweon, “CBAM: Convolutional block attention module,” 2018, arXiv:1807.06521.10.1007/978-3-030-01234-2_1 ↗
26. J. Nelson and B. Dwyer, “Roboflow: Give your software the power to see objects in images and video,” 2020. Accessed: Dec. 14, 2023. [Online]. Available: https://roboflow.com
27. T. DeVries and G. W. Taylor, “Improved regularization of convolutional neural networks with cut-out,” 2017, arXiv:1708.04552.
28. R-CNN for small object detection
29. A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” 2020, arXiv:2004.10934.
30. G. Jocher, “Ultralytics YOLO,” 2023. Accessed: Aug. 21, 2023. [Online]. Available: https://github.com/ultralytics/ultralytics
31. T. Wu, S. Tang, and R. Zhang, “CGNet: A light-weight context guided network for semantic segmentation,” 2019, arXiv:1811.08201.
32. Y. Wu and K. He, “Group normalization,” 2018, arXiv:1803.08494.10.1007/978-3-030-01261-8_1 ↗
33. J. Chen, S. Kao, and H. He, “Run, don’t walk: Chasing higher FLOPS for faster neural networks,” 2023, arXiv:2303.03667.10.1109/CVPR52729.2023.01157 ↗
34. A comprehensive review of object detection with deep learning10.1016/j.dsp.2022.103812 ↗
35. J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” 2016, arXiv:1612.08242.10.1109/CVPR.2017.690 ↗
36. Overfitting, model tuning, and evaluation of prediction performance
37. Z. Cai and N. Vasconcelos, “Cascade R-CNN: Delving into high quality object detection,” 2017, arXiv:1712.00726.10.1109/CVPR.2018.00644 ↗
38. C. Feng, Y. Zhong, and Y. Gao, “TOOD: Task-aligned one-stage object detection,” 2021, arXiv:2108.07755.10.1109/ICCV48922.2021.00349 ↗
39. G. Jocher, “YOLOv5 by Ultralytics. 2020,” Accessed: Apr. 11, 2024. [Online]. Available: https://github.com/ultralytics/yolov5
40. Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO series in 2021,” 2021, arXiv:2107.0843.
41. C. -Y. Wang, I. -H. Yeh, and H. -Y. Liao, “YOLOv9: Learning what you want to learn using programmable gradient information,” 2024, arXiv:2402.13616.10.1007/978-3-031-72751-1_1 ↗
42. A. Wang, H. Chen, and L. Liu, “YOLOv10: Real-time end-to-end object detection,” 2024, arXiv:2405.14458.
43. Y. Zhao, W. Lv, and S. Xu, “DETRs beat YOLOs on real-time object detection,” 2024, arXiv:2304.08069.10.1109/CVPR52733.2024.01605 ↗
44. Multi scale SAR image detection algorithm for ships based on improved YOLOv5
45. ShipRSImageNet: A large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images10.1109/JSTARS.2021.3104230 ↗
46. Side scan sonar image object detection method based on improved YOLOv5 network
47. Adaptive color attributes for real-time visual tracking