Efficient and rapid identification of substation equipment is a crucial part in substation safety status detection. To better fit the complex backgrounds and different target devices of the substation, the PSA module is introduced on the basis of YOLOv7 to realize the information interaction between local and global channels and improve the recognition accuracy of the model for devices of different scales. Combined with PConv and GSConv, a lightweight network is established to accelerate the inspection progress while ensuring model accuracy. Using Dyhead to embed three perceptions into a target detection head, improving target detection capabilities. Compared with the original YOLOv7 algorithm, the accuracy is improved by 3% and the model is reduced by 10%, which meets the requirements of efficient and rapid identification of substation equipment and provides a basis for subsequent substation equipment fault diagnosis.