SeRNet: Segmentation Helps Reconstruction for Anomaly Detection

With production growth and improvements in production speed in modern industries, accurate anomaly detection is becoming increasingly important to improve quality inspection efficiency and help minimize production costs. Existing reconstruction-based methods achieve promising anomaly detection results in some scenarios. However, when large-scale anomalies exist, the generalization ability of these methods is limited, and it is difficult to reconstruct the anomalies effectively into normal areas, which may lead to unsatisfactory results. To address this issue, this paper proposes a novel network architecture called SeRNet, which comprises three components: a segmentation sub-network, a reconstruction sub-network, and a repair module. SeRNet addresses the challenge of large-scale anomaly reconstruction by utilizing the segmentation sub-network for pre-segmentation, the repair module for repairing the large-scale anomalies using normal images similar to the test images, and the reconstruction sub-network for processing small-scale anomalies and anomalies in the repaired splices. Additionally, this paper introduces two methods for generating pseudo-anomalies at different scales. SeRNet leverages the advantages of both the segmentation sub-network, which is effective at segmenting large-scale anomalies, and the reconstruction sub-network, which can effectively reconstruct small-scale anomalies. Experiments on the MVTec AD industrial dataset demonstrate that SeRNet delivers outstanding performance, achieving an image-level AUROC score of 99.6.