An industrial product surface anomaly detection method based on masked image modeling

Current unsupervised industrial product surface anomaly detection methods suffer from poor reconstructed image quality and difficulty in detecting low-contrast anomalies, resulting in low anomaly detection accuracy. To address the above problems, we propose an unsupervised masked hybrid convolutional Transformer anomaly detection model, which forces the model to predict missing or edited regions based on unmasked information by introducing a mask reconstruction strategy, and utilises convolutional blocks and Transformer self-attention mechanism to extract the local features and global context of the image at different resolutions, enhancing the model's ability to understand the interrelationships among image parts and the overall structure. information to enhance the model's ability to understand the interrelationships between image parts and the overall structure, and to improve the reconstruction ability of the model; then a method based on Gaussian difference significance is proposed, which is combined with gradient magnitude similarity and colour difference to compare the differences between reconstructed and original images from multiple perspectives, and to improve the anomaly localisation performance of the model. We conducted extensive experiments on the industrial datasets MVTec AD and MTD to validate the effectiveness of the proposed method.