An identification method for device status combining video monitoring in substation main control rooms

The status of devices in substation main control rooms is jointly characterized by fine-grained components such as indicator lights, pressure plates, rotary knobs, display screens and alarm windows. To overcome the limitations of traditional single-frame detection in small object recognition, state continuity and component logical relations, this study proposes a device status identification model integrating improved YOLO target detection, bidirectional temporal modeling and graph relation reasoning. Based on fixed monitoring videos, the model performs state component annotation, local feature enhancement, multi-scale feature fusion and device status correlation graph construction to achieve device status judgment from component detection. Experimental results show that the proposed model achieves a device status accuracy of 96.28%±0.29%, an anomaly recall of 95.36%±0.41%, and a false alarm rate of 2.74%±0.18%. In real scenarios, the recall of state components reaches 96.34%±0.35% at a monitoring distance of 4 m, and the anomaly recall reaches 94.21%±0.46% under 20% occlusion. The proposed method provides an effective intelligent approach for substation video inspection and anomaly detection.