研究基于YOLO11的轻量级桥梁裂纹检测算法
Xuwei Dong1, Jiashuo Yuan1, Jinpeng Dai2,3
1Key Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
一个新的轻量级深度学习算法,YOLO11-桥梁检测 (YOLO11-BD),提高了桥梁裂检测的准确性和效率. 这种计算机视觉方法为智能桥梁维护提供了实时监控功能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 结构健康监测 结构健康监测
背景情况:
- 传统的桥梁裂检测方法缺乏效率和准确性.
- 基于深度学习的计算机视觉正在成为桥梁裂检测的热点.
- 确保桥梁安全和寿命需要有效的裂检测.
研究的目的:
- 提出一个轻量级和高效的桥梁裂检测算法.
- 为了提高特征提取和检测准确度,而不会增加模型大小.
- 为了实现实时检测和高计算效率的桥梁监控.
主要方法:
- 优化YOLO11模型以创建YOLO11-桥梁检测 (YOLO11-BD).
- 集成一个高效的多尺度调整 (EMSCA) 模块,以增强道和空间注意力.
- 引入一种轻量级检测头 (LDH),使用分组卷积来减少参数和计算.
主要成果:
- 与原来的YOLO11.11相比,YOLO11-BD提高了mAP50的3.1%和mAP50-95的4.8%.
- 全球GFLOP降低了19.05%,表明了显著的计算效率.
- 实现了超过每秒500的检测速度,证明了卓越的实时能力.
结论:
- YOLO11-BD算法提供了一种高效灵活的解决方案,用于使用遥感监控桥梁裂.
- 它的轻量级设计确保了智能桥梁管理的跨平台适应性.
- 提供可靠的技术支持,通过先进的计算机视觉来提高桥梁安全和维护.
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