OBC-YOLOv8:基于YOLOv8的改进的道路损坏检测模型
Shizheng Zhang1, Zhihao Liu1, Kunpeng Wang1
1Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
PeerJ. Computer science
|February 3, 2025
概括
一种新的道路损坏检测方法,OBC-YOLOv8,增强了路面困境识别. 这种方法通过利用先进的深度学习技术,提高了道路维护的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 道路基础设施管理 道路基础设施管理
背景情况:
- 有效的路面应急检测对于道路维护和安全至关重要.
- 现有的方法经常与复杂和多样化的损伤特征作斗争.
- 准确的道路状况评估需要强大而高效的检测系统.
研究的目的:
- 提出一种新的道路损坏检测方法,OBC-YOLOv8,用于改进路面损坏的识别.
- 提高道路损坏深度学习模型的特征提取能力和检测精度.
- 为道路维护和管理提供更有效,更可靠的解决方案.
主要方法:
- 拟议的OBC-YOLOv8模型整合了适应性特征学习的万维动态卷积 (ODConv).
- 将瓶变压器 (BoTNet) 集成到骨干中,同时提取全球和本地特征.
- 在部部分使用坐标注意力机制 (CA) 来完善检测并减少干扰.
主要成果:
- 在RDD2022-中国数据集中,OBC-YOLOv8模型表现出卓越的性能.
- 与基线模型相比,平均平均精度50 (mAP@0.5) 提高了1.8%.
- 在F1得分上表现出1.6%的改善,表明检测效率提高.
结论:
- OBC-YOLOv8在自动化路面应急检测方面取得了重大进展.
- 整合ODConv,BoTNet和CA有效地提高了模型性能.
- 这种方法为高效准确的道路维护策略提供了一个有前途的工具.
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