基于改进的YOLOv8s模型的道路缺陷检测
Jinlei Wang1, Ruifeng Meng2, Yuanhao Huang3,4
1School of Aviation, Inner Mongolia University of Technology, Hohhot, 010021, China.
Scientific reports
|July 20, 2024
概括
本研究介绍了一种增强的YOLOv8模型用于道路缺陷检测,提高了实时应用的准确性和效率. 优化的模型为道路维护检查提供了更好的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路工程工程的道路工程.
背景情况:
- 目前的道路缺陷检测方法缺乏准确性和速度,阻碍了有效的道路维护.
- 现有的模型在边缘部署和实时分析方面扎,限制了实际应用.
- 定期道路检查对于及时维护和基础设施寿命至关重要.
研究的目的:
- 开发一个改进的YOLOv8模型,用于准确高效地检测道路缺陷.
- 提高检测速度,并支持边缘部署实时道路监控.
- 解决当前道路维护方法的局限性.
主要方法:
- 设计了具有部分卷积的EMA快速块,以取代YOLOv8 C2f模块的瓶结构 (C2f-快速-EMA).
- 集成的SimSPPF取代SPPF,以提高模型的速度.
- 实现了Detect-Dyhead作为新的头部,以提高表现能力而不会增加计算负载.
主要成果:
- 与原来的YOLOv8.8相比,平均精度 (mAP@0.5) 提高了5.8%.
- 模型大小减少了22.33%,参数大小减少了23.03%,计算复杂度减少了21.68%.
- 在专门的道路缺陷检测数据集上表现出卓越的性能.
结论:
- 改进的YOLOv8模型在道路缺陷检测方面显著优于原始版本.
- 这些改进带来了更高效,更准确,更易于部署的道路维护解决方案.
- 这种优化的模型适用于道路检查中的实时边缘应用.
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