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改善YOLOv11用于海水质量监测和污染源识别
1School of Computer and Software Engineering, ZhengZhou Sias University, Zhengzhou, 451100, China. wangfang2024113@126.com.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种增强的YOLOv11模型用于海洋污染检测,提高了复杂的水下环境中的准确性和效率. 先进的模型有效地识别各种污染物,为环境监测提供了强大的解决方案.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
背景情况:
- 海洋污染对生态系统和人类健康构成重大威胁.
- 现有的你只看一次 (YOLO) 模型在检测海洋污染物方面面临挑战,原因是复杂的水下条件.
研究的目的:
- 开发一个改进的YOLOv11模型,用于准确和可靠的海洋污染检测.
- 提高模型适应不规则的污染物形状,不同尺度和背景干扰的能力.
主要方法:
- 整合可变形卷积网络版本4 (DCNv4) 以提高适应性.
- 实施海洋融合损失 (MFL) 以优化检测重量分配.
- 使用多尺度特征融合 (MFF) 与CNN和变压器进行增强的稳定性.
- 整合实例细分用于精细的边界检测.
主要成果:
- 改进的YOLOv11模型实现了90.2%的mAP50精度,推断速度为3.5ms.
- 在海洋污染检测方面表现优于YOLOv8和YOLOv10等先进模型.
- 在复杂的水下环境中证明了高精度和效率.
结论:
- 拟议的YOLOv11模型为智能海洋污染监测提供了一种高性能解决方案.
- 集成的DCNv4,MFL和MFF显著提高了检测准确性和稳定性.
- 这种方法为解决海洋污染造成的严重环境问题提供了有价值的工具.
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