In view of the problems such as missing detection and false detection in the reading detection of the pointer water meter with uneven illumination and atomized water meter dial, a reading detection method of the pointer water meter based on the improved YOLOv5s was proposed. Firstly, data augmentation methods such as Mosaic and Mixup were used to improve the generalization ability of the model. Secondly, the introduction of the bilateral feature pyramid network (BiFPN) module enabled higher-level feature fusion, enabling the full fusion of deep and shallow feature maps of water meter images, thereby improving the network's expressive power. Then, the convolutional block attention module (CBAM) was embedded to enhance the sub dial indication features of the pointer water meter in both channel and spatial dimensions. Finally, the complete intersection over union loss (CIoU Loss) function was replace with SIoU_Loss (scylla intersection over union loss) to improve the regression accuracy of bounding boxes. Improved algorithm mAP@0.5 reached 97.8%, an improvement of 3.2% compared to the YOLOv5s original network. The experimental results show that this algorithm can effectively improve the reading detection accuracy of pointer water meters.