Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research
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 accu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/244 |
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| author | Xiang Li Jun Zhao Changchang Zeng Yong Yao Sen Zhang Suixian Yang |
| author_facet | Xiang Li Jun Zhao Changchang Zeng Yong Yao Sen Zhang Suixian Yang |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-28be70f540b840b5913e03ecc87cbe61 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-28be70f540b840b5913e03ecc87cbe612025-01-10T13:21:20ZengMDPI AGSensors1424-82202025-01-0125124410.3390/s25010244Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation ResearchXiang Li0Jun Zhao1Changchang Zeng2Yong Yao3Sen Zhang4Suixian Yang5School of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaNational Institute of Measurement and Testing Technology, Chengdu 610056, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaNational Institute of Measurement and Testing Technology, Chengdu 610056, ChinaSchool of Big Data, Guizhou Institute of Technology, Guiyang 550003, ChinaSchool of Mechanical Engineering, Sichuan University, Chengdu 610065, ChinaWith 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.https://www.mdpi.com/1424-8220/25/1/244PMRRdeep learningdigital transformationimage processingpattern recognition |
| spellingShingle | Xiang Li Jun Zhao Changchang Zeng Yong Yao Sen Zhang Suixian Yang Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research Sensors PMRR deep learning digital transformation image processing pattern recognition |
| title | Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research |
| title_full | Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research |
| title_fullStr | Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research |
| title_full_unstemmed | Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research |
| title_short | Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research |
| title_sort | deep learning based pointer meter reading recognition for advancing manufacturing digital transformation research |
| topic | PMRR deep learning digital transformation image processing pattern recognition |
| url | https://www.mdpi.com/1424-8220/25/1/244 |
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