基于改进的YOLOv7的基础上,在河道中检测漂浮物体的小目标检测
Weifeng Yang1, Bing Zhang2, Su Guo3
1Anji County fusion media Center, Huzhou, 313300, P. R. China. Zhangbing_work@outlook.com.
Scientific reports
|March 1, 2026
概括
本研究引入了一种新的区域重叠检测 (ROD) 方法,使用最小卷曲YOLOv7 (MCY) 快速检测移动流中的小物体. 该方法在动态环境中显著提高了检测精度和平均精度 (mAP).
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 环境监测 环境监测
背景情况:
- 移动流的计算机视觉中的高率要求需要快速检测算法.
- 跨的界限框的变化导致小物体检测的精度较低.
- 在诸如河流这样的动态环境中检测小型漂浮物体是一个重大的挑战.
研究的目的:
- 引入一种新的区域重叠检测 (ROD) 方法,用于增强小目标检测.
- 解决现有方法在动态,流动的流中检测小物体的局限性.
- 使用修改后的YOLOv7架构来提高对象检测精度和融合速度.
主要方法:
- 实现一个区域重叠检测 (ROD) 方法与最小卷曲YOLOv7 (MCY) 架构集成.
- 使用典型的YOLO分类器来识别多个区域中最大的重叠区域.
- 在神经网络的最终训练层中,采用二次方法从最小卷积中提取最大的界限框.
- 修改YOLO架构,从卷积层中删除交叉区域以加快融合.
主要成果:
- 拟议的MCY-ROD方法准确地识别流动的小物体,具有高平均精度.
- 在检测小型漂浮物体方面获得了73.1%的平均平均精度 (mAP).
- 在动态河流环境中实现了70.2%的召回率,表现出强大的性能.
结论:
- 在动态环境中,MCY-ROD方法在小型目标检测方面取得了重大进展.
- 对YOLO架构的修改有效地加速了融合,并提高了检测精度 (mAP).
- 这种方法提供了一个可靠的解决方案,用于监控在现实世界的河流设置中小型漂浮物体.
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