Visual Anomaly Detection Through the Joint Entropy-Energy Optimization for High-Speed Railway Traction Substation

High-speed railway traction substations are critical facilities in high-speed rail systems, responsible for providing stable electric power to high-speed trains through the power supply system. However, anomalies endangering the traction substation occur from time to time. Only by detecting anomalies promptly, timely and targeted countermeasures can be adopted. Therefore, the timely detection of anomalies is essential for the safe operation of traction substations. Existing methods can only detect known anomalies; however, anomaly types are diverse, and abnormal data is difficult to collect, resulting in high uncertainty of anomalies. To address the issue of anomaly uncertainty in visual anomaly monitoring of traction substations, this article proposes an anomaly detection method based on out-of-distribution (OOD) detection through joint entropy-energy optimization. Specifically, we construct an anomaly scoring network (AS-Net) to jointly model semantic, entropy, and energy feature spaces. This allows for a more effective characterization of data distributions and differentiation between in-distribution normal data and OOD anomalies. We further construct the hybrid feature anomaly detection network (HFAD-Net) to fuse information from different perspectives of the AS-Net and detect anomalies. Experimental results on the traction substation dataset demonstrate that the proposed method can improve anomaly detection performance, with the AP increased by 30.81% and FPR95 reduced by 27.69% on average compared to other training methods. The final anomaly detection AP reaches 61.67%, surpassing multiple comparison methods. We also validated the generalizability and effectiveness of the proposed method on three benchmark datasets. Overall, this article provides an efficient and accurate anomaly monitoring method for traction substations.