Aiming at the problem that the digital Instrument Detection algorithm has poor real-time performance and poor generalization in edge equipment, an instrument detection-YOLO (ID-YOLO) model for substation digital instrument detection and recognition is proposed. The algorithm is based on YOLOv5 model. Firstly, a lightweight Li-YOLO backbone network is designed to extract image features, reduce network parameters, and improve real-time detection. Then, a bi-level routing attention module (BRAM) is designed to improve the precision of decimal point detection and the robustness and generalization of the network. Finally, the introduction of α-IoU loss can improve the detection accuracy of the model through more accurate IoU calculation. Experiments show that compared with other digital instrument detection and recognition methods based on deep learning, the proposed method has better accuracy and generalization on digital instrument recognition tasks with different display modes, and the detection speed of the model on the edge device can be improved from 6.87 frames /s to 8.77 frames /s while the detection accuracy is leading. Its real-time performance and detection accuracy can meet the engineering needs of intelligent data acquisition, detection and identification of actual substation.