通过增强功能融合,轻量级的强大检测人为漂浮碎片在杂和动态的水环境中
Yuanzhuo Zhong1, Jiaquan Wan2, Mingzhu Cao1
1College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China.
Scientific reports
|December 3, 2025
概括
这项研究介绍了BiDB-YOLOv8用于检测人为漂浮碎片 (AFD). 该模型在复杂的水环境中提供了更高的准确性和稳定性,并为现实世界水污染管理提供了高效率.
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:
- 精确检测人为漂浮碎片 (AFD) 对水污染管理至关重要.
- 目前的检测算法在水生环境中与环境稳定性和高计算需求作斗争.
- 在轻量级平台上部署受到现有方法计算成本的阻碍.
研究的目的:
- 开发一种高效,强大的算法,用于复杂水生环境中的AFD检测.
- 增强特征提取和集成,以提高检测准确度,特别是对于小目标.
- 引入一个新的数据集,Turbid-floater,用于训练和评估AFD检测模型.
主要方法:
- 拟议的BiDB-YOLOv8模型具有增强的功能处理架构.
- 使用多分支卷积块,以获得卓越的特征提取质量.
- 实施了高效的双向融合网络,以实现有效的多尺度特征集成.
- 构建并发布了Turbid-floater数据集,其中包括各种干扰场景.
主要成果:
- 与基线YOLOv8n相比,BiDB-YOLOv8显示了较好的检测准确度,参数增加最小.
- 实现了与较大的YOLOv8s模型相似的准确性,但计算成本显著降低.
- 该模型显示增强了区分小目标与杂背景的能力.
结论:
- BiDB-YOLOv8为AFD检测提供了精度,稳定性和效率之间的有利权衡.
- 拟议的模型适用于现实环境监测应用,包括在轻型平台上部署.
- 浮体数据集为推进AFD检测研究提供了宝贵的资源.
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