使用RFD-DETR检测铁路紧固件缺陷:一种基于变压器的轻量级实时方法
Huixiang Zhou1, Yuhao Liu1, Jian Wang1
1School of Information and Software Engineering, East China JiaoTong University, Nanchang, Jiangxi, China.
PloS one
|November 10, 2025
概括
一个新的深度学习模型RFD-DETR增强了实时铁路紧固件缺陷检测. 它提高了准确性和效率,为铁路安全检查提供了重大进步.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 铁路紧固件的手动检查容易出现错误,并且效率低下.
- 现有的深度学习模型 (例如,YOLO) 在缺陷特征提取,模型大小和计算需求方面存在局限性.
研究的目的:
- 引入RFD-DETR,这是一个优化的检测变压器模型,用于实时识别铁路紧固件缺陷.
- 为了提高多尺度特征提取,模型效率和缺陷检测准确度.
主要方法:
- 开发了三种新型模块的RFD-DETR:波段转换卷积 (WTConv) 用于多尺度的特征提取和计算减少.
- 集成交叉尺度特征融合 (CSPPDC) 与通道隔离注意力下采样 (CGAD) 进行精细的缺陷检测.
- 在部模块中内置了波纹转换特征升级 (WFU),用于增强特征融合.
主要成果:
- 在扩展数据集上,RFD-DETR在0.5的IOU值下实现了98.27%的平均平均精度 (mAP).
- 该模型与基线模型相比显示出更高的性能.
- 计算费用减少了18.8%,参数数量减少了14.7%.
结论:
- RFD-DETR提供了一种高效和高效的解决方案,用于实时检测铁路紧固件缺陷.
- 拟议的模型解决了现有方法的局限性,提高了准确性和减少了资源需求.
- 这一进步有助于通过自动化检查提高铁路轨道的稳定性和安全性.
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