# VSM-UNet: A Visual State Space Reconstruction Network for Anomaly Detection of Catenary Support Components

## TL;DR

This paper introduces VSM-UNet, a new deep learning model for detecting anomalies in railway catenary support components using a state space approach.

## Contribution

The novel VSM-UNet model combines a visual state space block with an asymmetric encoder-decoder structure for improved anomaly detection in railway components.

## Key findings

- VSM-UNet achieves an AUROC of 0.986 for detecting CSC loosening anomalies.
- The model processes data at 26.56 FPS, showing high efficiency in anomaly detection tasks.
- The method demonstrates effectiveness on components like positioning clamp nuts and U-shaped hoop nuts.

## Abstract

Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models have problems such as limited remote modeling capabilities and secondary computational complexity, it is difficult for existing deep learning anomaly detection methods to effectively exert their performance. The state space model (SSM) represented by Mamba is not only good at long-range modeling, but also maintains linear computational complexity. In this paper, using the state space model (SSM), we proposed a new visual state space reconstruction network (VSM-UNet) for the detection of CSC loosening anomalies. First, based on the structure of UNet, a visual state space block (VSS block) is introduced to capture extensive contextual information and multi-scale features, and an asymmetric encoder–decoder structure is constructed through patch merging operations and patch expanding operations. Secondly, the CBAM attention mechanism is introduced between the encoder–decoder structure to enhance the model’s ability to focus on key abnormal features. Finally, a stable abnormality score calculation module is designed using MLP to evaluate the degree of abnormality of components. The experiment shows that the VSM-UNet model, learning strategy and anomaly score calculation method proposed in this article are effective and reasonable, and have certain advantages. Specifically, the proposed method framework can achieve an AUROC of 0.986 and an FPS of 26.56 in the anomaly detection task of looseness on positioning clamp nuts, U-shaped hoop nuts, and cotton pins. Therefore, the method proposed in this article can be effectively applied to the detection of CSCs abnormalities.