CSEANet:跨阶段增强聚合网络,用于检测铁路钢结构桥梁的表面螺栓缺陷
Yichao Chen1, Yifan Sun2, Ziheng Qin1
1Beijing Jiaotong University, Beijing 100080, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
检测铁路螺栓缺陷对于桥梁安全至关重要. 基于YOLOv8的框架CSEANet通过使用新的预处理和网络模块来提高检测准确性,从而实现可靠的,实时的无人机检查.
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
- 结构健康监测 结构健康监测
背景情况:
- 铁路钢结构桥梁的手动检查是劳动密集型和主观的.
- 基于无人机 (UAV) 的现有方法在识别小物体和复杂背景方面面临挑战.
研究的目的:
- 开发基于YOLOv8的改进框架 (CSEANet) 以准确有效地检测铁路钢结构桥梁的表面螺栓缺陷.
- 在资源有限的环境中解决目前无人机检查技术的局限性.
主要方法:
- 实施了滑窗选择性注意力融合 (SAF) 预处理方法,以增强小目标表示和减少噪音.
- 引入了结合BSBlock和MBConvBlock的精细网络架构,以实现高效的功能提取.
- 开发了一种新的BoltFusionFPN模块,用于改进多尺度特征融合.
主要成果:
- 通过SAF预处理方法,平均精度 (mAP) 提高了0.404.
- CSEANet实现了一个高的mAP@50:95为0.952.
- 该框架证明了它适合在具有计算限制的环境中进行无人机检查.
结论:
- CSEANet提供了一种可靠的实时解决方案,用于使用无人机检测铁路桥上的螺栓缺陷.
- 拟议的框架加强了结构完整性的评估,并支持更安全的铁路运营和基础设施维护.
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