SNN Based Anomaly Detection Using ESVAE

In the manufacturing industry, Anomaly Detection (AD) technology is emerging as an essential element for equipment anomaly detection and quality improvement. In this paper, we implemented an SNN-based Efficient Spiking Variational Autoencoder (ESVAE) based anomaly detection model to improve the efficiency of anomaly detection in manufacturing and provide technological innovation, and conducted comparative experiments with ANNs-based VAE (ANN-VAE) and FSVAE models using MVTec AD dataset. ESVAE improved the average image-level receiver operating characteristic curve (ROCAUC) by 5.7% over Fully Spiking Variational Autoencoder (FSVAE) and the average pixel-level ROCAUC by 5.3% over ANN-VAE, while reducing the training time by about 8.1%. This demonstrates that the ESVAE model is both energy efficient and temporally robust. The proposed model is expected to make a significant contribution to real-time anomaly detection and quality inspection systems.