文献类型：期刊文献

英文题名：Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research

作者：Li, Xiang Zhao, Jun Zeng, Changchang Yao, Yong Zhang, Sen Yang, Suixian

第一作者：Li, Xiang

通信作者：Yang, SX[1];Zeng, CC[2]

机构：[1]Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China;[2]Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China;[3]Natl Inst Measurement & Testing Technol, Chengdu 610021, Peoples R China;[4]Guizhou Inst Technol, Sch Big Data, Guiyang 550003, Peoples R China

第一机构：Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China

通信机构：corresponding author), Sichuan Univ, Sch Mech Engn, Chengdu 610065, Peoples R China;corresponding author), Civil Aviat Flight Univ China, Sch Comp Sci, Guanghan 618307, Peoples R China.

年份：2025

卷号：25

期号：1

外文期刊名：SENSORS

收录：;EI(收录号：20250217672515);Scopus(收录号：2-s2.0-85214476618);WOS:【SCI-EXPANDED(收录号:WOS:001393930800001)】；

基金：This research was funded by the Fundamental Research Funds for the Central Universities: No. PHD2023-028; Research projects of civil aviation flight technology and flight safety engineering technology research center of Sichuan: No. GY2024-24D; Research projects of key laboratory of general aviation technology of Henan: No. ZHKF-240205; Research Project on Talent Policy in Developed Countries: No. 24H03006; the Open Project of State Key Laboratory of Public Big Data: No. PBD2023-36; National Key Research and Development Program: No. 2020YFB1707901.

语种：英文

外文关键词：PMRR; deep learning; digital transformation; image processing; pattern recognition

摘要：With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems.