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基于动态卷积的船舶检测模型和适应性融合网络,用于复杂的海上条件
Zhisheng Li1, Zhihui Deng1, Kun Hao1
1School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了YOLO-Vessel,这是一种增强的船舶检测模型,可以提高海上安全. 它通过处理复杂的背景和尺度变化,即使在恶劣的天气中,也能实现卓越的准确性和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 海事监督部门的监督工作
背景情况:
- 船舶检测对于海上安全至关重要,但面临着诸如错误检测,复杂背景和不同规模的挑战.
- 现有的车型在恶劣的天气条件下表现不佳.
研究的目的:
- 开发一个先进的船舶检测模型,YOLO-Vessel,以克服当前方法的局限性.
- 通过提高检测准确性和速度,加强海上安全和船舶监测.
主要方法:
- 开发了基于YOLOv7的YOLO-Vessel,结合了高效层聚合网络和全维动态卷积 (ELAN-ODConv) 来进行特征提取.
- 在头部网络中引入了空间到深度结构,以改善对小型船舶目标的检测.
- 实现了ASFFPredict,用于可靠的多尺度船舶目标检测.
主要成果:
- YOLO-Vessel 实现了 78.3% 的平均平均精度 (mAP),超过了 YOLOv7 的 2.3% 和 Faster R-CNN 的 11.6%.
- 该模型以8.0ms/frame的实时运行.
- 在恶劣的天气条件下表现出卓越的性能.
结论:
- YOLO-Vessel为实时船只检测提供了强大而有效的解决方案.
- 该模型显著提高了海上安全和船舶监测能力.
- 拟议的建筑创新解决了海上监视的关键挑战.
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