To enhance the real-time and robustness of digital instrument digital display area detection, an improved AdvancedEAST algorithm is proposed to complete the detection of substation digital instrument digital display area quickly and accurately. First, under the framework of the AdvancedEAST model, an ES-MobilenetV3 lightweight backbone network enhanced by the attention mechanism is constructed. By considering the influence of different layers of extracted features on the results, the ECA and multi-dimensional attention mechanism (ECA-SE) is introduced to The Bneck block of MobileNetv3 is improved to highlight key features while maintaining a balance between performance and complexity. A depthwise separable convolution is introduced in the neck network to reduce the computational complexity of the network and improve the detection speed. At the same time, the transfer learning strategy is used to improve the generalization ability of the model under small samples. Finally, the experimental verification was carried out on the constructed substation digital instrument dataset. The results showed that the proposed algorithm reduced the number of parameters of the model by 82% compared to the AdvancedEAST algorithm and increased the detection speed by nearly 2 times while ensuring detection accuracy.