| Citation: |
MA Tianlei, FU Jun, MA Qi, YANG Zhen, LIU Xinhao. Electric meter data detection based on global and local multi-scale context[J]. Journal of Applied Optics, 2024, 45(4): 804-811. DOI: 10.5768/JAO202445.0403006
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The detection of electricity meter data in the distribution box of a power system provides important data support for power management and safe operation. Traditional manual methods for reading electricity meter data are inefficient and prone to errors, while existing deep learning methods are limited in model application due to large model parameter sizes. To address these issues, a lightweight and robust real-time electricity meter detection method was proposed. The parameter size of the model was reduced by reducing the number of layers and channels in the feature extraction network, and the lightweight of the network was achieved. While reducing the network parameter size, the global context and local multi-scale context were introduced to ensure the network feature representation and fitting capabilities. The global context focused on the position of electricity meter data in the meter box, while the local multi-scale context adapted to different sizes of meter data. Experimental results show that the proposed network achieves higher accuracy and faster detection speed than other detection methods, even with smaller parameter sizes.
| [1] |
张慧. 基于轮廓提取的电能表视觉识别技术[J]. 自动化应用, 2011(8): 52-54.
ZHANG Hui. Watt-hour meter visual identity technology based on contour extraction[J]. Automation Application, 2011(8): 52-54.
|
| [2] |
王舒憬, 陈凯. 基于图像处理的智能电表显示缺陷自动检测系统设计[J]. 电测与仪表, 2016, 53(4): 63-68. doi: 10.3969/j.issn.1001-1390.2016.04.011
WANG Shujing, CHEN Kai. Automatic testing system design of smart meter based on computer vision[J]. Electrical Measurement & Instrumentation, 2016, 53(4): 63-68. doi: 10.3969/j.issn.1001-1390.2016.04.011
|
| [3] |
陈英, 蒋文峰, 杨丰玉, 等. 智能电能表数字识别算法研究[J]. 信息通信, 2020, 33(1): 17-21.
CHEN Ying, JIANG Wenfeng, YANG Fengyu, et al. Research on digits recognition algorithm of intelligent electric meter[J]. Information & Communications, 2020, 33(1): 17-21.
|
| [4] |
杨娟. 基于数字图像处理的电能表图像识别技术研究与实现[D]. 南京: 南京理工大学, 2012.
YANG Juan. Research and implementation of image recognition technology of watt-hour meter based on digital image processing[D]. Nanjing: Nanjing University of Science and Technology, 2012.
|
| [5] |
曾伟铭. 基于数字图像处理技术的智能电表读数识别方法研究[J]. 黑龙江电力, 2022, 44(5): 467-470.
ZENG Weiming. Research on reading recognition method of intelligent electric meter based on digital image processing technology[J]. Heilongjiang Electric Power, 2022, 44(5): 467-470.
|
| [6] |
潘毓生. 基于图像处理的智能电表读数识别方法的研究[D]. 天津: 天津理工大学, 2021.
PAN Yusheng. Research on the reading recognition method of smart meter based on image processing[D]. Tianjin: Tianjin University of Technology, 2021.
|
| [7] |
沈美丽. 数字电表图像的检测与识别[J]. 现代电子技术, 2022, 45(16): 110-114.
SHEN Meili. Detection and recognition of digital ammeter image[J]. Modern Electronics Technique, 2022, 45(16): 110-114.
|
| [8] |
尹建丰, 卫鑫, 顾雄伟, 等. 基于图像阈值优化及改进SVM的电表数字识别[J]. 计算机与现代化, 2023(5): 106-110. doi: 10.3969/j.issn.1006-2475.2023.05.017
YIN Jianfeng, WEI Xin, GU Xiongwei, et al. Digital identification of electric meter based on image threshold optimization and improved SVM[J]. Computer and Modernization, 2023(5): 106-110. doi: 10.3969/j.issn.1006-2475.2023.05.017
|
| [9] |
REDMON J, FARHADI A. YOLOV3: an incremental improvement[EB/OL]. (2018-04-08) [2023-07-05]. https://pjreddie.com/media/files/papers/YOLOv3.pdf.
|
| [10] |
WU W T, LIU H, LI L L, et al. Application of local fully convolutional neural network combined with YOLOv5 algorithm in small target detection of remote sensing image[J]. PLoS One, 2021, 16(10): e0259283. doi: 10.1371/journal.pone.0259283
|
| [11] |
LIU W, ANGUELOV D, ERHAN D, et al. SSd: single shot multibox detector[M]//Computer Vision–ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.
|
| [12] |
TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 10781-10790.
|
| [13] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
|
| [14] |
REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
|
| [15] |
HE K M, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 2961-2969.
|
| [16] |
高雪飞, 喻伟, 程飞, 等. 基于卷积神经网络的指针电表读数检测方法研究[J]. 电子乐园, 2021(12): 116-117.
GAO Xuefei, YU Wei, CHENG Fei, et al. Research on pointer electric meter reading detection method based on convolutional neural network[J]. Electronics Garden, 2021(12): 116-117.
|
| [17] |
张俊, 杨光, 胡东升, 等. 基于注意力机制与YOLOv3的电表自动读数算法[J]. 电力信息与通信技术, 2021, 19(12): 82-87.
ZHANG Jun, YANG Guang, HU Dongsheng, et al. An automatic reading algorithm of power meter based on YOLOv3 and attention mechanism[J]. Electric Power Information and Communication Technology, 2021, 19(12): 82-87.
|
| [18] |
SRIPANUSKUL N, BUAYAI P, MAO X Y. Generative data augmentation for automatic meter reading using CNNs[J]. IEEE Access, 2022, 10: 28471-28486. doi: 10.1109/ACCESS.2022.3157706
|
| [19] |
LAROCA R, BARROSO V, DINIZ M A, et al. Convolutional neural networks for automatic meter reading[J]. Journal of Electronic Imaging, 2019, 28(1): 1-14.
|
| [20] |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
|
| [21] |
YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[EB/OL]. (2016-04-30) [2023-07-05]. https://arxiv.org/pdf/1511.07122.
|
| [22] |
FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 3146-3154.
|
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