AC-YOLO:基于YOLO11的SAR图像的轻量级船舶检测模型
Rui He1, Dezhi Han1, Xiang Shen1,2
1College of Information Engineering, Shanghai Maritime University, Shanghai, China.
PloS one
|July 30, 2025
概括
本研究介绍了AC-YOLO,这是一款轻量级的合成孔径雷达 (SAR) 船舶检测模型,可以提高准确性并降低计算成本. 这种新型模型增强了小目标检测,并为边缘计算平台提供了可行的解决方案.
科学领域:
- 海洋科学与技术 海洋科学与技术
- 遥感和地缘空间分析.
- 环境监测中的人工智能
背景情况:
- 合成孔径雷达 (SAR) 对于海上应用至关重要,因为它具有全天候成像能力.
- 现有的SAR船舶检测算法在准确性和计算需求方面扎.
- 挑战包括各种目标尺度,模糊的特征和复杂的背景.
研究的目的:
- 开发一种新的,轻量级的SAR船舶检测模型 (AC-YOLO),解决准确性和计算成本的局限性.
- 为了提高小目标的检测和改善复杂SAR图像中的特征歧视.
- 在边缘计算平台上为海上监控提供有效的解决方案.
主要方法:
- 提出了AC-YOLO,这是一个基于YOLOv11.11的轻量级SAR船只检测模型.
- 设计了一种轻量级的跨尺度功能融合模块,用于自适应的多尺度信息集成.
- 开发了一种混合注意力增强模块,结合了卷积和自我注意力机制.
- 引入了对欧盟的最小点距离交叉点 (MPDIoU) 损失函数,以优化边界框回归.
主要成果:
- 与基线YOLOv11.11相比,AC-YOLO减少了30.0%的参数数量和15.6%的计算负载.
- 在SSDD数据集上实现了1.2%的平均精度 (AP) 改进.
- 在HRSID数据集上增加了1.5%的AP.
- 证明了复杂性和检测准确性的有效调和.
结论:
- AC-YOLO在SAR船只检测方面提供了显著的进步,平衡了效率和性能.
- 该模型的轻量级设计使其适合在资源有限的边缘计算平台上部署.
- 这项研究为利用SAR技术增强海上安全和管理提供了实际解决方案.
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