ArticleSensors (Basel, Switzerland)2023
Automatic Recognition Reading Method of Pointer Meter Based on YOLOv5-MR Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Lightweight YOLO object detectors for PET and HDPE classification in recycling facilities.Scientific reports · 2025Article
- Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research.Sensors (Basel, Switzerland) · 2025Article
- Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5.Sensors (Basel, Switzerland) · 2024Article
- Lightweight Detection Methods for Insulator Self-Explosion Defects.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
Meter reading is an important part of intelligent inspection, and the current meter reading method based on target detection has problems of low accuracy and large error. In order to improve the accuracy of automatic meter reading, this paper proposes an automatic reading method for pointer-type meters based on the YOLOv5-Meter Reading (YOLOv5-MR) model. Firstly, in order to improve the detection performance of small targets in YOLOv5 framework, a multi-scale target detection layer is added to the YOLOv5 framework, and a set of Anchors is designed based on the lightning rod dial data set; secondly, the loss function and up-sampling method are improved to enhance the model training convergence speed and obtain the optimal up-sampling parameters; Finally, a new external circle fitting method of the dial is proposed, and the dial reading is calculated by the center angle algorithm. The experimental results on the self-built dataset show that the Mean Average Precision (mAP) of the YOLOv5-MR target detection model reaches 79%, which is 3% better than the YOLOv5 model, and outperforms other advanced pointer-type meter reading models.
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