Character region extraction of wheel water meter based on object detection

Currently, research on automatic meter reading mainly focuses on meter reading recognition, while neglecting the fundamental role of counter detection in the entire automatic meter reading system. In fact, only by accurately locating the counter area can the influence of dial factors be completely eliminated, thus ensuring the accuracy and reliability of subsequent water meter reading recognition. In view of this phenomenon, the focus of this study is on the counter detection stage. Firstly, a target detection-based image skew correction method is proposed to solve the problem of image skew caused by shooting angle and other reasons. This method ensures the accuracy of subsequent counter area positioning and the neatness of cutting effect. Secondly, a semi-supervised target detection training method is proposed to solve the problem of time and manpower costs required in large-scale data situations. In addition, we have made publicly available a dataset containing 1070 water meter images for non-commercial purposes, which can be obtained from the Github11https://github.com/QuanhuiZhao/water-datasets.. Finally, we evaluated our model on three completely different datasets and compared it with the best positioning results of other models. The experimental results show that compared with other models, the proposed model in this paper has improved the positioning accuracy by 5.82%, 5.96%, and 9.20% on three datasets respectively. Furthermore, in the final visualization comparison, the model accurately identifies the counter region even when faced with complex real-world environments. •Innovative Approach: The manuscript introduces a novel image skew correction method based on object detection, specifically tailored for character region extraction in wheel water meters, enhancing the accuracy of automatic meter reading systems.•Target Detection: A semi-supervised target detection training approach is proposed to reduce the high time and labor costs associated with large-scale data annotation, improving efficiency in the field of automatic meter reading.•Dataset Contribution: The authors have created and made publicly available a dataset comprising 1070 water meter images for non-commercial use, facilitating further research and development in the area.•Performance Improvement: Experimental results demonstrate that the proposed model achieves significant improvements in positioning accuracy over existing models, with increases of 5.82%, 5.96%, and 9.20% on three