基于改进的YOLOv5s在复杂的近岸场景中的红外船舶检测
Xiuwen Liu1, Mingchen Liu1, Yong Yin1
1Key Laboratory of Marine Simulation and Control, Navigation College, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究介绍了CGSE-YOLOv5s,这是一种改进的算法,用于在复杂的近岸红外图像中检测船舶. 它在具有挑战性的环境中提高了准确性,这对海上安全和航运至关重要.
科学领域:
- 海洋技术的海洋技术
- 计算机视觉 计算机视觉 计算机视觉
- 遥感是一种远程传感.
背景情况:
- 在近海水域的航行安全对于航运经济至关重要.
- 船舶检测技术在密集,复杂的近岸环境中面临着挑战.
- 红外视觉比可见光提供了优越的性能,用于全天候目标检测.
研究的目的:
- 为复杂的近岸红外场景开发先进的船舶检测算法.
- 解决现有方法在密集的容器分布和杂乱的背景中的局限性.
- 提高船舶识别和分类的准确性和可靠性.
主要方法:
- 提出了CGSE-YOLOv5s算法,这是YOLOv5s的增强.
- 集成的对比度有限的自适应式直方图等级与高斯过用于边缘增强.
- 用基于Swin变压器的C3STR模块取代C3模块,以减少多个尺度的错误检测.
- 实施了高效通道注意力机制,以放大目标特征.
主要成果:
- CGSE-YOLOv5s实现了94.8%的平均平均精度 (mAP@0.5).
- 与标准YOLOv5s相比,表现出1.3%的改善.
- 在近岸红外场景中表现优于其他现有的船舶检测算法.
结论:
- 拟议的CGSE-YOLOv5s算法显著提高了在复杂的近岸红外环境中的船舶检测.
- 综合增强有效地解决了由密集的船只分布和复杂的背景所带来的挑战.
- 这项技术有助于提高航行安全和船舶经济的可持续发展.
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