在合成光圈雷达图像中应用改进的物体检测算法来检测和跟踪漏油
Haoluan Zhao1, Peng Zheng1, Shitao Peng1
1Key Laboratory of Environmental Protection Technology on Water Transport, Ministry of Transport, National Engineering Research Center of Port Hydraulic Construction Technology, Tianjin Research Institute for Water Transport Engineering, M.O.T., Tianjin 300456, China.
Marine pollution bulletin
|August 21, 2025
概括
这项研究引入了一种改进的YOLOv8模型,用于从合成光圈雷达 (SAR) 图像中检测和分类运行油泄露 (OOS). 该模型提高了识别泄漏源的准确性,这对于追踪海洋污染至关重要.
科学领域:
- 遥感技术
- 海洋污染的监测
- 人工智能
背景情况:
- 由于其不可预测的性质,运营性石油泄漏 (OOS) 带来了重大的海洋污染挑战.
- 目前的检测方法通常使用二进制分类,阻碍了源识别.
- 要将OOS与其他海洋现象区分开来,需要先进的分类技术.
研究的目的:
- 通过合成光圈雷达 (SAR) 图像开发高精度的OOS检测和分类模型.
- 将石油污染分为来源不明和OOS.
- 改善对石油污染源的追踪.
主要方法:
- 为OOS检测和分类提出了一种改进的YOLOv8模型,YOLOv8大选择性内核 (LSK).
- 集成LSK注意力模块以增强特征提取.
- 采用最小点距离的交点损失和切片辅助的超推理,以提高精度和大规模的图像处理.
主要成果:
- YOLOv8-LSK模型实现了94.2%的多类mAP50,比最好的二进制分类模型增加了7.44%.
- 多类mAP50-95的性能达到了71. 6%,比原始YOLOv8提高了3. 3%.
- 在三个案例研究中通过集成自动识别系统 (AIS) 数据成功确定泄漏源.
结论:
- 与现有方法相比,YOLOv8-LSK模型在OOS检测和分类方面提供了更高的性能.
- 该模型有助于智能检测和跟踪OOS,帮助识别污染源.
- 这项研究为推进海洋污染监测技术提供了宝贵的参考资料.
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