There are problems caused by the complicated recognition environment of a substation protection plate, the difficult segmentation of foreground and background, and the difficulty in detection of small targets. This paper proposes an enhanced YOLO algorithm for the state recognition of a substation protection plate. First, a local residual aggregation module is proposed to aggregate the local residual features of the stacked residual modules to strengthen the recognition ability in complex environments. Then, a spatial attention mechanism is embedded in the residual block and the spatial information is used to solve the problem that the foreground and background are difficult to segment. Finally, a cross-form space pyramid module is proposed to extract global context information. The labeled data set is used for verification. The experimental results show that the enhanced YOLO algorithm has significantly improved the status recognition effect of the protective plate compared with an improved algorithm. This work is supported by the National Natural Science Foundation of China (No. 52077120) “Research on Major Risk Analysis and Control Strategy by Reclosing for Large-scale Power transmission Channel” and the National Natural Science Foundation of China (No. 52007103) “Research on Time-Space Heterogeneous Dynamic Optimal Scheduling Method of the Combined Electrical and Heating Systems Considering the Dynamic Process of Heat Transfer in Heat Network System”.