YOLO-HPSD:基于YOLOv10的高精度船舶目标检测模型
Manlin Zhu1, Dezhi Han1, Bing Han2
1College of Information Engineering, Shanghai Maritime University, Shanghai, China.
PloS one
|May 12, 2025
概括
这项研究介绍了YOLO-HPSD,这是一种高精度的船舶目标检测算法. 它实现了海上应用的卓越准确性和速度,提高了安全性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 海事技术 在海事技术.
- 人工智能的人工智能
背景情况:
- 准确的船舶目标检测对于海上交通管理,智能港口和自主系统至关重要.
- 目前的方法需要在复杂的海上环境中提高高精度应用.
研究的目的:
- 开发基于YOLOv10.10的高精度船舶目标检测算法 (YOLO-HPSD).
- 提高在现实海洋场景中船舶检测的准确性和效率.
主要方法:
- 代注意力特征融合 (iAFF) 与C2F模块 (C2F_iAFF) 的集成,以优化特征融合.
- 引入混合本地频道注意力 (MLCA) 和BiFPN模块,以增强多级特征集成.
主要成果:
- 在船舶数据集上,YOLO-HPSD获得了97.88%的F1分数和98.86%的mAP@0.5分数.
- 与YOLOv10n相比,F1分数和mAP@0.5分别有1.22%和0.31%的改善.
- 实现了每张图像20.6ms的快速检测时间.
结论:
- YOLO-HPSD提供了一个强大的解决方案,用于高精度和高速的船舶目标检测.
- 该模型为实时海上监控和边缘计算开发提供了必要的技术支持.
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