Abstract: Image anomaly detection,which exploits patterns in normal images to detect abnormal images that do not conform to these patterns,plays a vital role in industrial production.Currently,one successful approach constructs a mask for normal images and uses a reconstruction network to predict the mask.The prediction error serves as the discriminant for image anomaly detection.However,this approach significantly increases model complexity when constructing the mask,compromising the reconstruction of normal image regions.To address this issue,this paper proposes a lightweight image anomaly detection method based on masked convolution kernels.This method applies a mask to the center of the convolution kernel and predicts the masked information through masked convolution and a channel-wise attention mechanism,effectively reducing the complexity of traditionalmas-king methods.A correction loss is introduced to constrain the reconstruction process of the masked predicted features,improving the reconstruction quality of normal regions.This method is validated on three industrial datasets MVTec,BTAD,and VISA,achieving AUROCs of 99.0%,93.7%,and 93.4%,respectively.This method can be combined with various existing reconstruction methods to effectively improve model performance without increasing model complexity.

Key words: Anomaly detection, Masked convolutional kernel, Unsupervised learning, Reconstruction network, Channel attention