改进了YOLOv5s和用于浮体检测的转移学习
Lei Guo1,2,3, Yiqing Zhang3, Qingqing Tian3
1Henan Water Conservancy Investment Group CO., LTD, Zhengzhou, China.
Science progress
|May 14, 2025
概括
这项研究引入了一种增强的YOLOv5s模型,用于有效和准确地检测水面上的漂浮碎片. 改进的模型显著提高了准确性,同时降低了计算负载,有助于监测水质.
科学领域:
- 环境科学 环境科学
- 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,如塑料瓶和死鱼,会降低水质,损害生态系统.
- 手动检测和清理是低效的,昂贵的,并带来风险.
研究的目的:
- 开发一个高效,准确和实时的系统来检测和分类水面上漂浮的物体.
- 改进现有的物体检测模型,用于环境监测应用.
主要方法:
- 一个改进的YOLOv5s模型是使用一个精心策划的浮动物体图像数据集开发的.
- 优化包括集成EfficientNetv2,高级上采样模块,双向特征金字塔网络,注意力机制 (SE,EMA) 和SIoU损失.
- 使用转移学习和数据增强 (SAGAN) 来提高模型性能.
主要成果:
- 与原始模型相比,增强的YOLOv5s模型实现了5.27个百分点的精度增加.
- 改进的模型将参数数量减少了53.9%,计算负载减少了21.3%,重量大小减少了54%.
- 废弃实验证实了个别改进的有效性.
结论:
- 拟议的模型提供了一种高效,准确和实时的解决方案,用于在水面上检测浮物.
- 这种方法对于监测水生环境和有效管理漂浮垃圾至关重要.
- 该研究为精确有效地检测和分类水表污染物提供了宝贵的见解.
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