基于改进的YOLOv8n模型的道路明显裂检测的研究
Wenyuan Xu1, Jianbo Xu1, Yongcheng Ji1
1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, Heilongjiang, China.
PloS one
|September 4, 2025
概括
这项研究加强了YOLOv8n的道路裂检测,通过整合注意力机制和先进特征融合提高了准确性. 优化的算法为路面裂识别提供了卓越的性能.
科学领域:
- 计算机视觉
- 人工智能
- 土木工程
背景情况:
- 道路表面裂检测对于基础设施维护至关重要.
- 现有的算法与可变的裂纹尺度和复杂的背景作斗争.
- 不准确的检测导致维护效率低下.
研究的目的:
- 增强YOLOv8n算法以改善道路表面裂的检测.
- 为了解决不够准确和错过检测的局限性.
- 开发一个更强大,更有效的裂识别系统.
主要方法:
- 卷积块注意模块 (CBAM) 与交叉阶段部分特征融合 (C2f) 模块的集成.
- 引入空间金字塔聚合快速跨阶段部分通道 (SPPFCSPC) 进行多级特征提取.
- 采用瘦模式和加权交叉对联盟 (WIOU) 损失函数.
主要成果:
- 增强的YOLOv8n算法显示了比基准值显著的改进.
- 平均精度 (mAP@50) 增长了1.8%,mAP@50-95增长了1.7%,回忆增长了1.8%.
- 这种改进的模型显示出优越的边界框回归和梯度缓解.
结论:
- 改进的YOLOv8n算法有效地提高了道路表面裂检测的准确性.
- 这些修改成功地处理了目标尺度变化和背景干扰.
- 这种先进的算法符合现代道路维护的要求.
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