软件导刊2025，Vol.24Issue(2)：163-171,9.DOI:10.11907/rjdk.241059
基于深度学习的指针式机械水表读数识别算法
Pointer Water Meter Reading Recognition Algorithm Based on Deep Learning
摘要
Abstract
Traditional pointer-style mechanical water meters predominantly rely on manual reading and recognition processes,which are of-ten time-consuming,incurs high labor costs,and are prone to high error rates.With the evolution of deep learning technologies,researchers have been applying these advancements in the field of water meter reading recognition.In this study,we propose a deep neural network-based algorithm for the recognition of readings from pointer-style mechanical water meters,referred to as the PWMR-DL algorithm.A specialized da-taset for pointer-style mechanical water meters was constructed for the training and testing of the algorithm.To detect and correct for sub-me-ter dials,the Mask R-CNN model was employed to locate and segment the dials,coupled with an efficient correction strategy to rotationally adjust individual sub-dials,thereby enhancing the robustness of recognition across various rotational angles and reducing errors.During the sub-dial reading recognition phase,the CA(Channel Attention)mechanism was introduced to refine the EfficientNet model,which signifi-cantly improved reading accuracy.By increasing the classification dimension to 20 classes,the algorithm refines the precision of judgments when the dial pointer is situated between numerals.Furthermore,by incorporating a correction logic related to the sequence of sub-dial read-ings,an effective method for generating readings was designed,substantially reducing errors.Experimental results demonstrate that the PWMR-DL algorithm achieves a 2.4%increase in precision for sub-dial reading recognition compared to the pre-improved EfficientNet mod-el,while only incorporating a small number of additional parameters,thereby preserving the model's lightweight characteristic.Notably,the PWMR-DL algorithm attained an overall recognition accuracy of 96.8%even under low-resolution imaging conditions.关键词
计算机视觉/EfficientNet/指针式水表/读数识别/CA注意力机制Key words
computer vision/EfficientNet/pointer water meter/reading recognition/CA mechanism分类
信息技术与安全科学引用本文复制引用
薛振豪,许书君,周哲帆,王敏,文向,喻珺岩,郭玉彬,李西明..基于深度学习的指针式机械水表读数识别算法[J].软件导刊,2025,24(2):163-171,9.基金项目
国家自然科学基金项目(61872152) （61872152）
广州市科技计划项目(201902010081) （201902010081）
