Aiming at the problem that the digital instrument detection algorithm has poor real-time performance and poor generalization in edge equipment, an instrument detection-you only look once (ID-YOLO) model for substation digital instrument detection and recognition was proposed. The proposed algorithm was based on YOLOv5 model. Firstly, a lightweight light weight-YOLO (LW-YOLO) backbone network was designed to extract image features, reduce network parameters, and improve real-time detection. Then, a bi-level routing attention module (BRAM) was designed to improve the precision of decimal point detection and the robustness and generalization of the network. Finally, the loss function α-IoU was introduced, the detection accuracy of the model was improved by setting different adjustable parameter α to obtain more accurate calculation of the intersection ratio between the real frame and the predicted frame. The results 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.