VSM-UNet:一个可视状态空间重建网络,用于对连锁支元件的异常检测
Shuai Xu1, Jiyou Fei1, Haonan Yang2
1School of Zhan Tianyou, Dalian Jiaotong University, Dalian 116028, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究介绍了VSM-UNet,这是一个新的深度学习模型,用于检测铁路连锁支元件 (CSC) 的异常. VSM-UNet有效地识别了CSC的松,提高了铁路安全和维护效率.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在连锁支元件 (CSC) 中检测异常对于铁路状况监测至关重要.
- 现有的深度学习模型 (CNN,变压器) 与微妙的异常特征和计算复杂性作斗争.
- 国家空间模型 (SSM) 提供强大的长距离建模,具有线性复杂性.
研究的目的:
- 提出一个新的深度学习模型,VSM-UNet,用于有效检测CSC松动的异常.
- 利用SSM的优势,改善视觉异常检测中的远程依赖模型.
- 为了提高检测微妙的异常特征在CSCs.
主要方法:
- 开发了基于UNet架构的VSM-UNet,其中包含一个视觉状态空间 (VSS) 块.
- 使用了一个不对称的编码器-解码器结构,具有补丁合并和扩展操作.
- 集成了CBAM注意力机制和基于MLP的异常分数计算模块.
主要成果:
- VSM-UNet模型在检测CSC松动异常方面表现出有效性和合理性.
- 实现了0.986.9的接收器运行特征曲线 (AUROC) 下的高面积.
- 在特定的CSC组件上达到26.56的每秒 (FPS).
结论:
- 拟议的VSM-UNet,学习策略和评分方法对于CSC异常检测是有效的.
- 与现有方法相比,VSM-UNet框架显示出显著的优势.
- 这种方法适用于实际应用,用于检测连锁支元件的异常.
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