RDAD: A reconstructive and discriminative anomaly detection model based on transformer

Given the shortcomings of low detection accuracy and poor generalization performance of most current surface defect detection methods for industrial products, this paper proposes a reconstructive and discriminative anomaly detection model. The proposed method uses squeeze-and-excitation block to assign the attention of feature channels to enhance the sensitivity of related features and improve the ability of the model to learn normal and anomaly boundaries. In addition, channel transformer is introduced in the encoder-decoder, so that the decoder better fuses the features in the encoder and reduces the semantic gap, and enhances the segmentation ability of anomalous regions of the model. The model is only trained with normal samples, and completes the localization of anomalous regions while detecting anomalies. Experiments are conducted on the challenging MVTec anomaly detection and Magnetic Tile Defect data sets. Compared with the current state-of-the-art unsupervised anomaly detection methods, the model not only improves the accuracy of anomaly detection, but also has better generality.