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JACIII Vol.28 No.1 pp. 206-215
doi: 10.20965/jaciii.2024.p0206
(2024)

Research Paper:

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Reading Recognition Method of Mechanical Water Meter Based on Convolutional Neural Network in Natural Scenes

Jianqi Li*1,*2 ORCID Icon, Jinfei Shen*3, Keheng Nie*4 ORCID Icon, Rui Du*1,*2, Jiang Zhu*2 ORCID Icon, and Hongyu Long*2

*1Department of Communication and Electric Engineering, Hunan University of Arts and Science
No.3150 Dongting Road, Changde, Hunan 415000, China

*2Hunan Province Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft
No.3150 Dongting Road, Changde, Hunan 415000, China

*3College of Information Science and Engineering, Jishou University
No.120 South Renmin Road, Jishou, Hunan 416000, China

*4Hunan Academy of Building Research Co., Ltd.
No.7 Wenchuang Road, Changsha, Hunan 410014, China

Received:
February 8, 2023
Accepted:
October 2, 2023
Published:
January 20, 2024
Creative Commons License
Keywords:
water meter reading, convolution neural network, automatic meter reading, object detection
Abstract

To satisfy the demand for real-time and high-precision recognition of mechanical water meter readings in natural scenes, a reading recognition method for mechanical water meters based on you only look once version 4 (YOLOv4) is proposed in this paper. First, a focus structure is introduced into the feature extraction network to expand the receptive field and reduce the loss of original information. Second, a ghost block cross stage partial module is constructed to improve the feature fusion of the network and enhance the feature representation. Finally, the loss function of YOLOv4 is improved to further enhance the detection accuracy of the network. Experimental results show that the mAP@0.5 and mAP@0.5:.95 of the proposed method are 97.9% and 77.3%, respectively, which are 1.6% and 6.0% higher, respectively, than those of YOLOv4. Additionally, the number of parameters and computation amount of the proposed method are 48.6% and 36.8% lower, respectively, whereas its inference speed is 27% higher. The proposed method is applied to assist meter reading, which significantly reduces the workload of on-site meter-reading personnel and improves work efficiency. The datasets used are available at https://github.com/914284382/Mechanical-water-meter.

Wearable mechanical water meter assisted meter reading system

Wearable mechanical water meter assisted meter reading system

Cite this article as:
J. Li, J. Shen, K. Nie, R. Du, J. Zhu, and H. Long, “Reading Recognition Method of Mechanical Water Meter Based on Convolutional Neural Network in Natural Scenes,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 206-215, 2024.
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Last updated on Apr. 22, 2024