Visual structural anomaly detection based on lightweight image restoration network
Qi-liang Wu, Dong-Ce fei · 2024
Currently, reconstruction-based visual anomaly detection tends to use more complex models to improve the accuracy of the model. This paper proposes a lightweight image restoration network to reduce the network model's parameters and computational complexity, and puts forward the feature similarity loss to reduce the impact of noise on the accuracy of anomaly detection discrimination. Experimental results show that the proposed method uses fewer parameters and computational complexity, achieving results close to RIAD.