Intercity Rail Transit Platform Anomaly Detection Using Door Tracking-Based Key Frame Extraction and AnoDet Network

Abstract

The anomalies in the risk space between platform doors and train will threaten the safety of intercity rail transit. However, performing anomaly detection tasks with an accurate grasp of the time gap between the train doors closed and the train departure is challenging. The scarcity of anomalous samples and the lack of prior knowledge of anomalies also pose considerable obstacles to anomaly detection. To solve the aforementioned problems, a method based on key frames extraction and a two-stage Transformer network is proposed. The switch statuses of the train door are extracted by the door tracking algorithm, which effectively provides key frames for subsequent anomaly detection. An end-to-end two-stage Transformer, which is named AnoDet, is proposed for unsupervised anomaly localization and supervised multi-classification. The AnoDet is based on an image masking strategy, dual-channel network, knowledge migration base and anomaly object classification set prediction loss. The unsupervised localization model first utilizes the masking strategy to remove a large number of image patches that may contain anomalies in the inputs. Then, it trains a dual-channel network based on the remaining normal patches to inpaint anomalous inputs into normal images. The supervised classifier proposes a knowledge migration base to provide prior knowledge of anomalies and alleviate the scarcity of anomalous samples. It also introduces a set prediction loss to achieve a natural connection with the unsupervised model. In the intercity rail station anomaly dataset, our method achieves 97.8% precision and 96.6% recall for anomaly localization and obtains 88.9% mAP for multi-classification, outperforming the state-of-the-arts.