FD2 - YOLO:基于YOLO的频域双流网络,用于破裂检测.
Junwen Zhu1, Jinbao Sheng1,2, Qian Cai1,2
1Nanjing Hydraulic Research Institute, Nanjing 210029, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
一个新的频域双流YOLO (FD 2 -YOLO) 网络通过融合频率和空间域特征来准确检测水泥裂. 这种方法在复杂的场景中增强了结构完整性监测和公共安全.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在水泥基础设施中检测裂对于结构完整性和公共安全至关重要.
- 现有的单个骨干方法在复杂环境中难以检测细小或可变裂.
研究的目的:
- 提出一个新的网络,FD2-YOLO,用于准确高效地检测水泥裂.
- 通过整合频率和空间域信息来增强特征提取.
- 在具有挑战性的场景中提高裂纹检测性能.
主要方法:
- 开发了一个双脊柱架构,集成频率域 (边缘,纹理) 和空间域 (语义) 特性.
- 引入了动态域间特征融合 (DIFF) 模块,用于使用大型内核卷积和哈达马德运算进行自适应性特征融合.
- 提出了DIA-Head模块与可变形交互注意 (DIA) 模块,以关注裂纹纹理和几何变形特征.
主要成果:
- 在RDD2022数据集上,FD 2 -YOLO实现了最先进的性能,超过了现有的YOLO模型.
- 改进包括+1.3%的mAP50,+1.1%的mAP50-95,+1.8%的回忆和+0.5%的精度.
- 在UAV-PDD2023数据集上,FD 2 -YOLO实现了67.9%的mAP50和75.9%的精度,性能优于轻量级和基于变压器的探测器.
结论:
- 在现实世界和空中成像场景中,FD 2 -YOLO在水泥裂检测方面表现出卓越的有效性和稳定性.
- 拟议的双流架构和融合模块显著提高了裂特征提取和检测精度.
- 该方法为基础设施健康监测和安全提供了一个有希望的解决方案.
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