Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning
Source abstract
Abstract
To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Object Detection Techniques · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
Department of State-Owned Assets and Laboratory Management, Tianjin University of Commerce, No. 409 Guangrong Road, Beichen District, Tianjin 300134, China
Funding
National Science Foundation of China · Tianjin Science and Technology Plan Project · Tianjin Municipal Education Commission Scientific Research Plan Project
References (29)
Show source-provided references
1. New identification method of linear pointer instrument10.1007/s11042-022-13403-z ↗
2. Automatic reading recognition of pointer barometer based on machine vision10.1117/1.JEI.31.5.051415 ↗
3. Automatic pointer meters recognition system based on line scan vision10.1088/1361-6501/ac8b9c ↗
4. A robust and high-precision automatic reading algorithm of pointer meters based on machine vision10.1088/1361-6501/aaed0a ↗
5. Pointer-type instrument positioning method of intelligent inspection system for substation10.1117/1.JEI.31.1.013001 ↗
6. Li, D.H., Li, W.X., Yu, X., Gao, Q., and Song, Y. (2021). Automatic reading algorithm of substation dial gauges based on coordinate positioning. Appl. Sci., 11.10.3390/app11136059 ↗
7. Intelligent pointer meter interconnection solution for data collection in farmlands10.1016/j.compag.2021.105985 ↗
8. Real-time reading system for pointer meter based on YolactEdge10.1080/09540091.2023.2241669 ↗
9. An industrial meter detection method based on lightweight YOLOX-CAlite10.1109/ACCESS.2022.3229874 ↗
10. Zhang, C.L., Shi, L., Zhang, D.D., Ke, T., and Li, J.R. (2023). Pointer meter recognition method based on Yolov7 and hough transform. Appl. Sci., 13.10.3390/app13158722 ↗
11. Liu, H.L., Wang, J.L., and Ma, B. (2022). Instrument pointer recognition scheme based on improved CSL algorithm. Sensors, 22.10.3390/s22207800 ↗
12. An intelligent vision recognition method based on deep learning for pointer meters10.1088/1361-6501/acb80b ↗
13. A YOLOv3-based industrial instrument classification and reading recognition method
14. A detection and recognition system of pointer meters in substations based on computer vision10.1016/j.measurement.2019.107333 ↗
15. A pointer meter recognition method based on virtual sample generation technology10.1016/j.measurement.2020.107962 ↗
16. A robust approach to reading recognition of pointer meters based on improved mask-RCNN10.1016/j.neucom.2020.01.032 ↗
17. Automatic recognition system of pointer meters based on lightweight CNN and WSNs with on-sensor image processing10.1016/j.measurement.2021.109819 ↗
18. Wang, L., Wang, P., Wu, L.H., Xu, L.J., Huang, P., and Kang, Z.L. (2021). Computer vision based automatic recognition of pointer instruments: Data set optimization and reading. Entropy, 23.10.3390/e23030272 ↗
19. Intelligent reading recognition method of a pointer meter based on deep learning in a real environment10.1088/1361-6501/ac4079 ↗
20. Scale value guided Lite-FCOS for pointer meter reading recognition10.1088/1361-6501/acf23a ↗
21. Agac, S., and Incel, O.D. (2023). On the use of a convolutional block attention module in deep learning-based human activity recognition with motion sensors. Diagnostics, 13.10.3390/diagnostics13111861 ↗
22. Xie, F., Lin, B.J., and Liu, Y.C. (2022). Research on the coordinate attention mechanism fuse in a YOLOv5 deep learning detector for the SAR ship detection task. Sensors, 22.10.3390/s22093370 ↗
23. Hyperspectral image classification model using squeeze and excitation network with deep learning
24. Squeeze-and-excitation networks10.1109/TPAMI.2019.2913372 ↗
25. Two dimensionality reduction techniques for SURF based face recognition10.1016/j.procs.2016.05.222 ↗
26. Vehicle seat detection based on improved RANSAC-SURF algorithm10.1142/S0218001421550041 ↗
27. Speeded-up robust features (SURF)10.1016/j.cviu.2007.09.014 ↗
28. Zunair, H., and Ben Hamza, A. (2021). Sharp U-Net: Depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med., 136.10.1016/j.compbiomed.2021.104699 ↗
29. Application of U-Net and optimized clustering in medical image segmentation: A review