SSAPN: Spectral–Spatial Anomaly Perception Network for Unsupervised Vaccine Detection

Abstract

Vaccines are the most significant and effective way to prevent disease and safeguard human health. However, it is easy to produce or mix foreign matters during the manufacturing process. Moreover, foreign matters are extremely faint that it is difficult to obtain images and detect them accurately. To tackle imaging challenges, in this paper, we built a hyperspectral imaging system to construct a first-of-its-kind HSI dataset with pixel-level annotation for vaccine anomaly detection, where the vaccine comes from the actual pharmaceutical company. To address the problem of low detection accuracy, we propose a spectral-spatial anomaly perception module joint with an unsupervised Auto-Encoder network (SSAPN), in which nonlinear features learned from the encoder are divided into non-overlapping patches and mapped to efficiently encode spectral and spatial feature information. The spectral-spatial MLP module consists of continuous and alternating spectral MLP with spatial MLP, which achieves spectral with spatial perception in the global receptive field, captures long-range dependencies, and extracts the most discriminative spectral-spatial features. Experimental results show that our SSAPN model outperforms other state-of-the-art anomaly detection methods in terms of both detection and generalization performance. This work will help speed up the production process in the vaccine pharmaceutical industry and ensure vaccine quality.