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Academic Journal of Computing & Information Science, 2023, 6(12); doi: 10.25236/AJCIS.2023.061204.

An Improved Method for Digital Water Meter Reading Area Segmentation Based on U~2-Net


Rongrong Fei, Runze Cheng, Bin Yao, Feng Tian, Liuyang Gao, Zemu Men

Corresponding Author:
Rongrong Fei

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, China


With the continuous development of intelligent technology, the recognition of water meter readings has brought convenience to people's lives. By automatically reading water meter data, work efficiency can be improved. However, due to the varying shapes and sizes of the recognition areas, it is difficult to achieve good recognition results. Therefore, in response to the problem of varying sizes of water meter reading recognition areas, this paper proposes a method for digital water meter reading recognition area segmentation based on an improved U2-Net. Firstly, in terms of capturing data features, an improved Double-RSU module based on the RSU module is designed. This module increases depth and complexity compared to the original RSU module, thereby improving generalization and robustness. Secondly, in terms of model training, a combination of cross-entropy loss function, Jaccard coefficient and Dice loss function is used to comprehensively evaluate the entire model by considering binary classification, segmentation accuracy and segmentation area overlap. Experimental results show that the proposed method has good performance in MIoU, MAE and F1Score indicators, with the MIoU indicator increasing from 0.878 to 0.91, the MAE indicator increasing from 0.001 to 0.0009, and the F1Score indicator increasing from 0.9431 to 0.9455. These results indicate that the improved model has better performance compared to the original U2-Net model and can more accurately perform water meter reading area segmentation.


Salient Object Detection, Deep Learning, Image Segmentation, U2-Net

Cite This Paper

Rongrong Fei, Runze Cheng, Bin Yao, Feng Tian, Liuyang Gao, Zemu Men. An Improved Method for Digital Water Meter Reading Area Segmentation Based on U~2-Net. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 12: 33-44. https://doi.org/10.25236/AJCIS.2023.061204.


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