Metro Anomaly Detection Based on Light Strip Inductive Key Frame Extraction and MAGAN Network

Abstract

Abnormal objects in the risk space between the platform door and train will threaten the safe operation of the metro transportation system. However, selecting the right time to perform anomaly detection during the process of metro in and out station is the primary problem. The trouble of lacking abnormal samples, the high imbalance of abnormal categories, and the defects of abnormal regeneration are not well resolved in most reconstruction-based anomaly detection methods. To handle the aforementioned issues, a method based on light strip inductive key frame extraction and patch-level unsupervised network is proposed. The light strip inductive feature is designed to simulate the status of train door movement, which contributes to extracting key frames for anomaly detection. An unsupervised network, which is named metro anomaly generative adversarial network (MAGAN) and based on dilate fine-grained generator, multipatch discriminator, and local attention reconstruction loss, is proposed for anomaly classification and localization. Our network only utilizes normal samples for training and greatly suppresses the reconstruction of abnormalities by dilate fine-grained generators during testing. The discriminator and local attention reconstruction loss promote the interaction of global and local information in the image and provide a new multipatch-based anomaly localization scheme. The proposed method is tested on the metro risk space anomaly dataset, and the AUCs of anomaly classification and localization achieve 97.2% and 96.4%, respectively, which outperforms those of the state-of-the-art methods.