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Real-time detection algorithm for digital meters based on multi-scale feature fusion and GCS

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Abstract

Aiming at the problems of insufficient feature fusion, large number of network parameters and low target saliency in the current digital meter detection algorithm, a digital meter detection algorithm based on YOLOv5s is designed. First, a new feature fusion structure Bi-Directional Feature Pyramid Network Based on Multi-Scale Feature Fusion is designed to realize the full fusion between different scale feature maps and improve the detection accuracy; second, a new convolutional module Ghostconv Combined Channel Shuffle, is designed to realize the lightweight design of the network and meet the requirements of real-time substation detection tasks; finally, to improve the network’s ability to characterize the instrumented digits, the Convolutional Block Attention Module is introduced in the backbone network to further enhance the network performance. Experiments are carried out on the homemade dataset, and the experimental results show that the algorithm proposed in this paper improves the average accuracy by 2.84–98.58% compared with the original network; the amount of network parameters is reduced by 26.4%, and the detection speed is improved by 25 FPS, and the detection time for each image is only 0.012 s. Compared with other digital meter detection algorithms, the network performance and the number of parameters also have a great advantage.

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Data Availability

The code involved in this paper will not be disclosed for the time being due to the need for subsequent research, and will be available for others to use after the overall project is completed.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (NSFC, Grant no. 52074213), and by the National key research and development program in China ( Grant no. 2021YFE0105000).

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Authors and Affiliations

  1. Xi’an University of Science and Technology, Xi’an, 710054, China

    Zhaoming Hao, Xiaoqiong Zhang, Hongyan Li, Meng Xu, Ziyang Zhang, Zhan Wang & Weifeng Wang

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  1. Zhaoming Hao
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  2. Xiaoqiong Zhang
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  3. Hongyan Li
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  4. Meng Xu
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  5. Ziyang Zhang
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  6. Zhan Wang
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  7. Weifeng Wang
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Contributions

HZ: Put forward suggestions for paper revision; ZX: The main algorithm design and improvement optimization and writing papers; LH: Provide the main ideas and suggestions of the thesis; XM: Collect part of the data needed for the paper; ZZ: Draw part of the model diagram; WZ: Provide experimental equipment; WW: Provide financial support; all the authors reviewed the paper.

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Correspondence to Xiaoqiong Zhang.

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Hao, Z., Zhang, X., Li, H. et al. Real-time detection algorithm for digital meters based on multi-scale feature fusion and GCS. J Real-Time Image Proc 21, 35 (2024). https://doi.org/10.1007/s11554-023-01408-2

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