The automatic reading of pointer instrument by camera is easily affected by complex environment, different camera angles and other factors, and it is difficult to balance the detection speed and detection accuracy in practical applications. Therefore, this paper proposes a pointer instrument reading algorithm based on key point detection. ResNet18 is used as the backbone network, the residual blocks in the last two stages and subsequent fully connected layers are abandoned, and a lightweight feature fusion network is designed according to the characteristics of the pointer meter panel, while introducing a pose refine machine ( PRM) that improves model performance. Finally, using the obtained three key point information of the dial circle center, the zero scale line, and the current pointer scale, the reading calculation is completed by the angle method. The experimental results show that, the reading error of the algorithm in this paper is only 0. 506%, and the speed can reach 53 frames/ second, which is more accurate than the traditional algorithm; compared with other similar algorithms, the proposed algorithm can still achieve high accuracy prediction of pointer key points with fewer parameters and computational complexity, fully proving the effectiveness of the proposed algorithm.