研究YOLOv11在铁路轨道紧固件中结合DHSA BRA和PPA模块的性能,检测铁路轨道紧固件缺陷的性能
Chengwei Zhang1, Jiawei Zhu2, Yihao Ma3
1College of Engineering, Zhejiang Normal University, Yingbin Avenue, Jinhua, 321005, China. ZhangCW233666@163.com.
Scientific reports
|July 29, 2025
概括
本研究介绍了YOLO-DRPA,这是一种用于铁路轨道紧固件检查的先进物体检测算法. 它通过整合新的注意力模块显著提高了在具有挑战性的条件下的准确性,提高了铁路安全.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 铁路工程 铁路工程是指铁路工程.
背景情况:
- 铁路轨道紧固件的手动检查正在被深度学习对象检测所取代.
- 当前的算法在恶劣的天气和低对比度环境中表现出较低的准确性.
研究的目的:
- 开发一个强大而高效的轨道紧固件缺陷检测算法.
- 为了提高铁路基础设施维护对象检测的准确性.
主要方法:
- 一个新的轨道紧固件缺陷检测算法,YOLO-DRPA,是基于YOLOv11.1开发的.
- 集成的动态范围的直方图表自我注意 (DHSA) 对于噪声的稳定性.
- 综合双级路由注意力 (BRA) 提高效率,并行补丁意识注意力 (PPA) 提高多级特征提取.
主要成果:
- YOLO-DRPA实现了94.6%的平均平均精度 (mAP@0.5) 和80.7%的mAP@0.5:0.95.
- 在各自的指标上,与YOLOv11n相比,表现出1.8%和4.0%的改善.
- 与其他流行的物体检测算法相比,展示了竞争性性能.
结论:
- 拟议的YOLO-DRPA算法显著提高了轨道紧固件检查的检测精度和效率.
- DHSA,BRA和PPA模块的集成解决了复杂条件中的局限性.
- YOLO-DRPA显示了提高铁路安全和运营效率的巨大潜力.
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