Efficient visual anomaly detection model with adaptive wavelet transform

Existing unsupervised surface anomaly detection models have shown promising results. However, research on lightweight and fast anomaly detection models is relatively scarce, thereby impeding their practical application in industrial scenarios where rapid detection is required. Following the approach of reconstruction-based detection methods, we introduce EawT, an efficient anomaly detection model. EawT incorporates a novel pseudo-anomaly synthesis method and a depthwise separable convolutional structure to enhance network lightweighting. Additionally, considering that some studies indicate that Deep Neural Networks (DNNs) may exhibit preferences for certain frequency components during the learning process, potentially affecting the robustness of learned features, we propose an Adaptive Wavelet Transform module designed to modulate frequency components with transfer difficulties in latent space, thereby allowing for dynamic adjustment of feature information to adapt to various requirements. Furthermore, we propose wavelet loss to assist in the reconstruction of original images. Our method achieves comparable detection accuracy to existing reconstruction-based models on the VisA and BTAD datasets, with a frame rate of 228 FPS on a 3090Ti GPU and a lower parameter count, demonstrating the effectiveness of the proposed approach.