MAS-YOLOv11:基于YOLOv11的改进的水下物体检测算法
Yang Luo1, Aiping Wu2, Qingqing Fu1
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
MAS-YOLOv11通过改进特征表示和融合来增强水下目标检测. 这种新型模型在复杂的水生环境中显著提高了准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 水下目标检测面临着诸如可见性差,遮蔽和尺度变化等挑战.
- 现有的模型与复杂的背景和重叠的目标作斗争.
研究的目的:
- 开发一种改进的模型,用于强大而准确的水下目标检测.
- 在具有复杂干扰的具有挑战性的水生环境中提高性能.
主要方法:
- 引入了C2PSA_MSDA模块,用于使用扩展注意力的增强多尺度特征表示.
- 实现了自适应空间特征聚变检测头 (ASFFHead),用于强大的多尺度物体检测.
- 开发了一个带有动态样本权重的幻灯片损失函数,以改善硬样本学习.
主要成果:
- 在DUO数据集上取得了显著的性能增长:回忆增长3.7%,F1得分提升3%.
- 在DUO上获得了77.4%的mAP@50和55.1%的mAP@50-95,表现优于基线.
- 在 RUOD 数据集上证明了跨领域的概括性,在 RUOD 数据集上达到 76% mAP@50.
结论:
- MAS-YOLOv11为水下目标检测任务提供了实质性的改进.
- 拟议的改进有效地解决了诸如阻塞和尺度变化等挑战.
- 该模型显示了在不同的水下数据集中强大的概括能力.
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