Meter-YOLOv8n: A Lightweight and Efficient Algorithm for Word-Wheel Water Meter Reading Recognition

To address the issues of low efficiency and large parameters in the current word-wheel water meter reading recognition algorithms, this paper proposes a Meter-YOLOv8n algorithm based on YOLOv8n. Firstly, the C2f component of YOLOv8n is improved by introducing an enhanced inverted residual mobile block (iRMB). It enables the model to efficiently capture global features and fully extract the key information of the water meter characters. Secondly, the Slim-Neck feature fusion structure is employed in the neck network. By replacing the original convolutional kernels with GSConv, the model's ability to express the features of small object characters is enhanced, and the number of parameters in the model is reduced. Finally, Inner-EIoU is used to optimize the bounding box loss function. This simplifies the calculation process of the loss function and improves the model's ability to locate dense bounding boxes. The experimental results show that, compared with the original model, the precision, recall, mAP@0.5, and mAP@0.5:0.95 of the improved model have increased by 1.7%, 1.2%, 3.4%, and 3.3% respectively. Meanwhile, the parameters, FLOPs, and model size have decreased by 0.56M, 2.6G, and 0.7MB respectively. The improved model can better balance the relationship between detection performance and computational complexity. It is suitable for the task of recognizing word-wheel water meter readings and has practical application value.

@article{Qiao2025,
  title     = {Meter-YOLOv8n: A Lightweight and Efficient Algorithm for Word-Wheel Water Meter Reading Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shichao Qiao and Yuying Yuan and Ruijie Qi},
  doi       = {10.14569/IJACSA.2025.0160422},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160422}
}