AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This addresses anomaly detection for high-speed train maintenance, but it is incremental as it adapts existing deep learning techniques to a new two-image comparison approach.
The paper tackles anomaly detection in high-speed train images by reframing it as a difference detection problem between two images taken at different times, using a deep feature difference network (AnoDFDNet) that combines Vision Transformer and convolutional neural networks, achieving superior results on three collected datasets.
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identified with a single image using classification, segmentation, or object detection methods, the proposed method detects abnormal difference between two images taken at different times of the same region. In other words, we cast anomaly detection problem with a single image into a difference detection problem with two images. The core idea of the proposed method is that the 'anomaly' usually represents an abnormal state instead of a specific object, and this state should be identified by a pair of images. In addition, we introduced a deep feature difference AD network (AnoDFDNet) which sufficiently explored the potential of the Vision Transformer and convolutional neural networks. To verify the effectiveness of the proposed AnoDFDNet, we collected three datasets, a difference dataset (Diff Dataset), a foreign body dataset (FB Dataset), and an oil leakage dataset (OL Dataset). Experimental results on above datasets demonstrate the superiority of proposed method. Source code are available at https://github.com/wangle53/AnoDFDNet.