Diff-Fastener: A Few-Shot Rail Fastener Anomaly Detection Framework Based on Diffusion Model

Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions. However, unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images. In this work, we have established a rail fastener anomaly detection framework called Diff-Fastener, the diffusion model is introduced into the fastener detection task, half of the normal samples are converted into anomaly samples online in the model training stage, and One-Step denoising and canonical guided denoising paradigms are used instead of iterative denoising to improve the reconstruction efficiency of the model while solving the problem of excessive smoothing. DACM (Dilated Attention Convolution Module) is proposed in the middle layer of the reconstruction network to increase the detail information of the reconstructed image; meanwhile, Sparse-Skip connections are used instead of dense connections to reduce the computational load of the model and enhance its scalability. Through exhaustive experiments on MVTec, VisA, and railroad fastener datasets, the results show that Diff-Fastener achieves 99.1% Image AUROC (Area Under the Receiver Operating Characteristic) and 98.9% Pixel AUROC on the railroad fastener dataset, which outperforms the existing models and achieves the best average score on MVTec and VisA datasets. Our research provides new ideas and directions in the field of anomaly detection for rail fasteners.