Water meter pointer reading recognition method based on target-key point detection

• Published in: Flow measurement and instrumentation Vol. 81; p. 102012
• Main Authors: Zhang, Qingqi, Bao, Xiaoan, Wu, Biao, Tu, Xiaomei, Jin, Yuting, Luo, Yuan, Zhang, Na
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2021
• Subjects: Key point detection; RFB-net; Target detection; Water meter pointer recognition; YOLOv4-tiny; YOLOv4-tiny; RFB-net; Key point detection; Water meter pointer recognition; Target detection
• ISSN: 0955-5986; 1873-6998
• DOI: 10.1016/j.flowmeasinst.2021.102012

With the rapid development of video image technology and fifth-generation mobile network technology, the automatic verification of mechanical water meters has become an increasingly important topic in smart cities. Although much research has been done on this subject, the efficiency and accuracy of existing water meter pointer reading technology can still be improved. This paper proposes a new water meter pointer reading recognition method based on target-key point detection. Our method consists of a target detection module and a key point detection module. The target detection module uses a modified YOLOv4-Tiny network to detect and classify the areas where the dials and pointers are located in water meter images with distinct characteristics. The key point detection module is used to detect the key point of the pointer image. In this module, the structure of the RFB-Net network is improved to introduce multiple layers of low-level feature information, therefore, it can make full use of the information between multi-scale feature layers for key point detection. In addition to, aiming at the problem of dial rotation, a method of establishing a right-angle coordinate system based on key point is proposed to realize pointer reading. The whole method proposed in this paper is compared to the Hough transform feature matching algorithm and traditional machine learning algorithms through experiments which test the detection and recognition of the water meter dial, pointer and key points. The experiment results show that the missed detection rate of the model in this paper is 1.88% and 1.07% for the dial region and the pointer region, respectively. And the accuracy rate reaches 98.68%, the average processing time per image is 0.37 s. This implies that the water meter inspection task is completed quickly and accurately with strong robustness. Thanks to the lightweight algorithm of our approach, the model can also be fully automated and easily deployed on mobile devices. •This paper presents a target-key point detection-based water meter pointer reading recognition method for automatic reading of water meter instruments. The method in this paper combines the latest target detection technology and key point detection technology.•A novel pointer reading method based on the structure of key point detection is proposed.•The network is improved according to the dataset of practical application scenarios. Experiments prove that the improvements in this paper improve the accuracy of recognition.•A comprehensive experimental comparison between the method in this paper and previous methods shows that the method in this paper can achieve automatic reading of water meters quickly and accurately.