SDC-YOLOv8:通过注意力增强特征学习和自适应特征重建来检测道路缺陷的改进算法
Hao Yang1, Yulong Song1, Yue Liang2
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了SDC-YOLOv8,这是一种用于检测道路缺陷的先进算法. 它显著提高了对小路面缺陷的准确性,提高了交通安全和维修效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路基础设施管理 道路基础设施管理
背景情况:
- 现有的物体检测算法与小的道路缺陷和复杂的环境条件作斗争.
- 不准确的道路缺陷检测损害了交通安全和及时修复.
研究的目的:
- 开发一个改进的基于YOLOv8的算法,用于准确高效地检测道路缺陷.
- 为了提高在具有挑战性的照明和背景场景下检测小路面缺陷.
主要方法:
- 拟议的SDC-YOLOv8算法集成SPPF-LSKA模块用于多尺度特征表示和DySample动态上抽样用于自适应特征重建.
- 集成的坐标注意模块可以在复杂的条件下提高空间定位的准确性.
- 使用快速空间金字塔聚合与大可分离内核注意力 (SPPF-LSKA) 和Dysample动态上采样.
主要成果:
- 在公共漏洞数据集上,SDC-YOLOv8实现了78.0%的mAP@0.5,81.0%的精度和70.7%的回忆.
- 该模型以85 FPS的实时性能,超过了基线YOLOv8n模型.
- 在mAP@0.5中实现了2.0个百分点的改进,在Precision中提高了3.3%,在Recall中提高了1.8%,F1得分为75.5%.
结论:
- SDC-YOLOv8有效地提高了对道路缺陷的小目标检测精度.
- 该算法保持实时处理能力,为智能道路缺陷检测提供了实用解决方案.
- 提出的方法为道路维护和交通安全应用提供了显著的进步.
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