改进了YOLOv8-SST,用于在复杂的水环境中准确检测小浮物
Huizhen Dong1, Jianjun Li2, Xi He3
1College of Civil Engineering and Architecture, Zhejiang University of Water Resources and Electric Power, Hangzhou, China.
PloS one
|January 8, 2026
概括
这项研究介绍了YOLOv8-SST,这是一个改进的算法,用于检测水面上的小型浮物. 它通过解决照明变化和水的扭曲来提高复杂水环境中的准确性.
科学领域:
- 环境监测环境监测环境监测
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 漂浮物体检测对于水环境保护至关重要.
- 现有的算法难以应对照明变化,水的扭曲以及在水环境中检测小物体.
- 需要强大的算法来改善水质监测和安全.
研究的目的:
- 开发一个改进的算法,YOLOv8-SST,用于更好地检测水面上的小型浮物.
- 为应对复杂水生环境带来的挑战,包括照明变化和水面扭曲.
- 减少水体中小物体的错误和错误检测.
主要方法:
- 开发了一个C2fBF (C2f-BiFormer) 模块,以便在下采样过程中保留细粒度的特征.
- 实现了一个内部完整交叉点在欧盟 (内部-CIoU) 损失函数,用于灵活的界限框调整.
- 将Adam优化器替换为Sophia优化器,以提高模型的概括性.
- 集成的FloW-Img,WSODD和自主收集的数据用于全面的培训和验证.
主要成果:
- 与基线YOLOv8n.相比,YOLOv8-SST表现出了优异的性能.
- 在平均平均精度 (mAP) @0.5.5.实现了3.1%的增长.
- 在mAP@0.5:0.95.5中实现了5.0%的增长.
- 该算法在在具有挑战性的自然水环境中检测小物体方面表现出有效性和稳定性.
结论:
- 拟议的YOLOv8-SST算法显著改善了水生环境中的小型浮物检测.
- 集成C2fBF模块,内部CIoU损失和Sophia优化器可以提高检测准确性和模型稳定性.
- 这种方法为有效的水环境监测和保护提供了一个有希望的解决方案.
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