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在铁路轨道缺陷检测中应用YOLO11模型与空间金字塔扩展卷积 (SPD-Conv) 和有效挤压刺激 (EffectiveSE) 融合
Weigang Zhu1, Xingjiang Han1, Kehua Zhang2
1College of Engineering, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
一个改进的"只看一次" (YOLO) 对象检测算法增强了铁路轨道缺陷检测. 这种方法提高了识别关键轨道异常的效率和准确性.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 对象检测算法越来越多地用于铁路轨道缺陷检测.
- 现有的方法面临着低效率和准确度不足的挑战.
研究的目的:
- 开发一个改进的"只看一次" (YOLO) 对象检测算法,用于改进铁路轨道缺陷检测.
- 解决当前轨道检查方法的效率和准确性的局限性.
主要方法:
- 修改了YOLO11骨干与空间金字塔扩展卷积 (SPD-Conv) 进行改进,以提高低分辨率和小物体检测.
- 集成的有效挤压刺激 (EffectiveSE) 注意力机制,以增强特征表示.
- 将一个小目标检测头添加到子网络中,用于多层次的目标捕获.
主要成果:
- 在轨道紧固件数据集上达到95.9%的mAP@0.5,在轨道表面数据集上达到89.5%的mAP@0.5.
- 与原始YOLO11模型和其他物体检测算法相比,表现出更高的性能.
- 显著提高了轨道缺陷检测的效率和准确性.
结论:
- 增强的YOLO11算法有效地改善了铁路轨道缺陷检测.
- 拟议的修改在识别轨道异常方面提供了卓越的性能.
- 这种方法为铁路基础设施检查提供了更有效,更准确的解决方案.
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