YOLOv7-CSAW用于海上目标检测
Qiang Zhu1, Ke Ma1, Zhong Wang1
1School of Computer Science and Technology, Hefei Normal University, Hefei, China.
本研究介绍了YOLOv7-CSAW,这是一种增强的海上搜索和救援算法,可以显著改善小目标检测. 新模型实现了更高的准确性,并在复杂的海上环境中减少了虚假阴性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 海洋技术 海洋技术
背景情况:
- 由于复杂的环境和小目标,海上搜救 (SAR) 行动面临着低检测率和高错误负面的挑战.
- 目前的目标检测算法在海上SAR中扎着稳定性和通用性.
研究的目的:
- 为海上SAR行动开发一个改进的目标检测算法.
- 提高在具有挑战性的海上条件下检测小目标的准确性和稳定性.
主要方法:
- 提出了YOLOv7-CSAW,这是基于YOLOv7.7的增强算法.
- 集成的K-means++用于箱优化,C2f模块用于梯度流,SimAM用于小目标感知,ASFF用于特征融合,以及WIoU损失函数.
主要成果:
- 与YOLOv7基线相比,YOLOv7-CSAW在平均平均精度 (mAP) 中取得了10.73%的改善.
- 在海上SAR数据集上,与传统的深度学习算法相比,检测性能显著改善.
结论:
- 在复杂的海上场景中,YOLOv7-CSAW显著提高了在复杂的海上场景中检测小目标的准确性和稳定性.
- 该算法有效地解决了海上SAR的问题,提高了检测率并减少了虚假负面.
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