一个增强的YOLOv8模型用于准确检测固体漂浮废物
Juxing Di1, Kaikai Xi1, Yang Yang2
1Hebei University of Architecture, Information Engineering College, Zhangjiakou, 075000, China.
Scientific reports
|July 11, 2025
概括
本研究介绍了ES-YOLOv8,这是一个增强的YOLOv8s模型,用于检测漂浮废物. 它提高了在具有挑战性的水环境中检测小,不规则形状的物体的检测精度.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在水面上检测漂浮废物存在挑战,因为物体大小小小,形状不规则,以及背景干扰.
- 现有的方法在复杂的水生环境中难以准确识别和定位.
研究的目的:
- 开发一个增强的物体检测模型,以改善漂浮废物识别.
- 解决在水生环境中检测小规模,不规则形状的浮动碎片的局限性.
主要方法:
- 提出了ES-YOLOv8,这是基于YOLOv8s的增强模型,通过"160-80-40-20"框架优化功能融合.
- 集成了高效多尺度注意力 (EMA) 模块,用于增强小物体的特征提取.
- 采用了新的Shape-IoU损失函数,以提高不规则目标的边界框回归精度.
主要成果:
- 与基线YOLOv8s相比,ES-YOLOv8显示了显著的改善,mAP@0.5增加了5.4%,mAP@0.5:0.95在定制数据集上增加了6.1%.
- 对比实验证实了该模型在最先进的方法上具有优越性.
- 对公共数据集的验证证实了ES-YOLOv8算法的稳定性和通用性.
结论:
- ES-YOLOv8为智能水治理和浮动废物管理提供了高精度,低功耗的解决方案.
- 该模型在特征提取和本地化方面的改进为生态和工程应用提供了有前途的技术进步.
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