• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

基于轻量级YOLO-v4模型的变电站数字仪表检测识别

华泽玺 施会斌 罗彦 张子原 李威龙 唐永川

华泽玺, 施会斌, 罗彦, 张子原, 李威龙, 唐永川. 基于轻量级YOLO-v4模型的变电站数字仪表检测识别[J]. 西南交通大学学报, 2024, 59(1): 70-80. doi: 10.3969/j.issn.0258-2724.20210544
引用本文: 华泽玺, 施会斌, 罗彦, 张子原, 李威龙, 唐永川. 基于轻量级YOLO-v4模型的变电站数字仪表检测识别[J]. 西南交通大学学报, 2024, 59(1): 70-80. doi: 10.3969/j.issn.0258-2724.20210544
HUA Zexi, SHI Huibin, LUO Yan, ZHANG Ziyuan, LI Weilong, TANG Yongchuan. Detection and Recognition of Digital Instruments Based on Lightweight YOLO-v4 Model at Substations[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 70-80. doi: 10.3969/j.issn.0258-2724.20210544
Citation: HUA Zexi, SHI Huibin, LUO Yan, ZHANG Ziyuan, LI Weilong, TANG Yongchuan. Detection and Recognition of Digital Instruments Based on Lightweight YOLO-v4 Model at Substations[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 70-80. doi: 10.3969/j.issn.0258-2724.20210544

基于轻量级YOLO-v4模型的变电站数字仪表检测识别

doi: 10.3969/j.issn.0258-2724.20210544
基金项目: 国家重点研发计划(2020YFB1711902)
详细信息
    作者简介:

    华泽玺(1968—),男,副教授,博士,研究方向为轨道交通智慧运维、传感器与智能检测、监测,E-mail:huazexi@163.com

  • 中图分类号: TP391.41;TP183

Detection and Recognition of Digital Instruments Based on Lightweight YOLO-v4 Model at Substations

  • 摘要:

    为了在变电站实际场景中准确获取数字仪表读数,智能管控变电站的安全风险,同时推动变电站智能化发展,以实际场景中变电站数字仪表作为研究对象,综合考虑实时性及准确度等,提出一种基于轻量级YOLO-v4模型的变电站数字仪表检测识别方法. 首先,通过从鄂尔多斯变电站实际拍摄变电站数字仪表图像数据,使用Albumentations框架对数字仪表图像进行数据扩充,构建变电站数字仪表目标检测数据集;然后,以YOLO-v4网络为基础,结合注意力机制构建一个有效通道注意(efficient channel attention,ECA)改进的深度可分离卷积模块(ECA-bneck-m);最后,提出一个轻量级YOLO-v4模型,进行模型大小与性能的对比实验. 实验结果表明:本文方法可以在几乎不损失检测准确度的情况下,将整个模型存储大小压缩为原先的1/5,同时将模型推理速度从24.0帧/s提升至36.9帧/s,其实时性能够满足实际变电站检测识别的工程需要.

     

  • 图 1  深度学习数字仪表检测识别方法总体框架

    Figure 1.  General framework of deep learning-based method for detection and recognition of digital instruments

    图 2  模板匹配方式示意

    Figure 2.  Illustration of template matching

    图 3  YOLO-v4模型框架

    Figure 3.  Framework of YOLO-v4 model

    图 4  bneck模块示意

    Figure 4.  Structure of bneck module

    图 5  H-Swish函数和Mish函数曲线

    Figure 5.  H-Swish and Mish function curves

    图 6  SE模块示意

    Figure 6.  Structure of SE module

    图 7  ECA模块示意

    Figure 7.  Structure of ECA module

    图 8  ECA-bneck-m模块示意

    Figure 8.  Structure of ECA-bneck-m module

    图 9  SPP层结构

    Figure 9.  Structure of SPP layer

    图 10  轻量级YOLO-v4模型结构示意

    Figure 10.  Structure of lightweight YOLO-v4 model

    图 11  Albumentations数据扩充效果

    Figure 11.  Data expansion results of Albumentations

    图 12  伽马变换预处理效果示意

    Figure 12.  Preprocessing results of Gamma transformation

    图 13  轻量级YOLO-v4模型的学习曲线

    Figure 13.  Learning curves of lightweight YOLO-v4 model

    图 14  数字仪表检测与读数识别结果示意

    Figure 14.  Recognition results of digital instrument detection and reading

    表  1  图像数据扩充结果

    Table  1.   Image data expansion results

    数据集数字仪表数字字符总计
    原数据集15711201 2772
    扩充数据集5000500010000
    下载: 导出CSV

    表  2  k-means预选框聚类结果

    Table  2.   k-means clustering results of prior box

    模型特征层
    13 × 1326 × 2652 × 52
    仪表检测
    模型
    (204, 149)(84, 174)(5, 16)
    (221, 448)(128, 227)(21, 36)
    (288, 144)(174, 479)(71, 131)
    字符识别
    模型
    (159, 191)(94, 127)(14, 24)
    (163, 270)(127, 167)(42, 62)
    (297, 876)(131, 633)(70, 308)
    下载: 导出CSV

    表  3  SPP层不同池化尺度性能对比结果

    Table  3.   Performance comparison of SPP layer at different pooling scales

    池化尺度mAP/%
    {3 × 3, 5 × 5}99.75
    {5 × 5, 7 × 7}99.69
    {7 × 7, 9 × 9}99.78
    {7 × 7, 11 × 11}99.74
    {5 × 5, 9 × 9}99.68
    下载: 导出CSV

    表  4  不同网络模型大小对比结果

    Table  4.   Comparison results of different model sizes

    网络模型参数量/个模型大小/MB
    YOLO-v4 (DarkNet-53)63986151244.0
    YOLO-v4 (bneck)1401871953.8
    YOLO-v4 (bneck-m)1401871953.8
    YOLO-v4 (ECA-bneck-m)1250646348.0
    下载: 导出CSV

    表  5  不同深度学习目标检测模型对比结果

    Table  5.   Comparison of different deep learning detection models

    网络mAP/%FPS/(帧·s−1
    Faster-RCNN83.886.0
    YOLO-v399.6430.0
    YOLO-v499.8024.0
    轻量级YOLO-v4 (bneck)99.5833.7
    轻量级YOLO-v4 (bneck-m)99.7535.6
    轻量级YOLO-v4 (ECA-bneck-m)99.7836.9
    下载: 导出CSV

    表  6  轻量级YOLO-v4(ECA-bneck-m)测试结果

    Table  6.   Lightweight YOLO-v4 (ECA-bneck-m) test results

    类别P/%R/%F1
    字符 0 识别99.8399.651.00
    字符 1 识别99.4398.870.99
    字符 2 识别98.58100.000.99
    字符 3 识别100.00100.001.00
    字符 4 识别98.56100.000.99
    字符 5 识别96.5899.300.98
    字符 6 识别95.2798.600.97
    字符 7 识别99.05100.001.00
    字符 8 识别97.54100.000.99
    字符 9 识别100.0099.251.00
    仪表检测97.22100.000.99
    数显区域定位98.25100.000.99
    下载: 导出CSV
  • [1] CUI X X, FANG H, YANG G Q, et al. A new method of digital number recognition for substation inspection robot[C]//2016 4th International Conference on Applied Robotics for the Power Industry (CARPI). Jinan: IEEE, 2016: 1-4.
    [2] 卜令正,王洪栋,朱美强,等. 基于改进卷积神经网络的多源数字识别算法[J]. 计算机应用,2018,38(12): 3403-3408.

    BU Lingzheng, WANG Hongdong, ZHU Meiqiang, et al. Multi-source digit recognition algorithm based on improved convolutional neural network[J]. Journal of Computer Applications, 2018, 38(12): 3403-3408.
    [3] 郭兰英,韩睿之,程鑫. 基于可变形卷积神经网络的数字仪表识别方法[J]. 计算机科学,2020,47(10): 187-193.

    GUO Lanying, HAN Ruizhi, CHENG Xin. Digital instrument identification method based on deformable convolutional neural network[J]. Computer Science, 2020, 47(10): 187-193.
    [4] 陈刚,胡子峰,郑超. 基于特征检测的数字仪表数码快速识别算法[J]. 中国测试,2019,45(4): 146-150.

    CHEN Gang, HU Zifeng, ZHENG Chao. Fast recognition algorithm for digital instruments based on feature detection[J]. China Measurement & Test, 2019, 45(4): 146-150.
    [5] 郭爽. 数码管数字仪表自动识别方法的研究[J]. 通信技术,2012,45(8): 91-93.

    GUO Shuang. Study on automatic identification method of digital tube[J]. Communications Technology, 2012, 45(8): 91-93.
    [6] 高菊,叶桦. 一种有效的水表数字图像二次识别算法[J]. 东南大学学报(自然科学版),2013,43(增1): 153-157.

    GAO Ju, YE Hua. An effective two-times recognition algorithm of meter digital image[J]. Journal of Southeast University (Natural Science Edition), 2013, 43(S1): 153-157.
    [7] ZHOU C H, ZHOU J Y, YU C, et al. Multi-channel sliced deep RCNN with residual network for text classification[J]. Chinese Journal of Electronics, 2020, 29(5): 880-886. doi: 10.1049/cje.2020.08.003
    [8] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.
    [9] TANG Z L, LIU Q Q, WU M J, et al. WiFi CSI gesture recognition based on parallel LSTM-FCN deep space-time neural network[J]. China Communications, 2021, 18(3): 205-215. doi: 10.23919/JCC.2021.03.016
    [10] 黄继鹏,史颖欢,高阳. 面向小目标的多尺度Faster-RCNN检测算法[J]. 计算机研究与发展,2019,56(2): 319-327.

    HUANG Jipeng, SHI Yinghuan, GAO Yang. Multi-scale faster-RCNN algorithm for small object detection[J]. Journal of Computer Research and Development, 2019, 56(2): 319-327.
    [11] 林刚,王波,彭辉,等. 基于改进Faster-RCNN的输电线巡检图像多目标检测及定位[J]. 电力自动化设备,2019,39(5): 213-218.

    LIN Gang, WANG Bo, PENG Hui, et al. Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN[J]. Electric Power Automation Equipment, 2019, 39(5): 213-218.
    [12] 王粉花,黄超,赵波,等. 基于YOLO算法的手势识别[J]. 北京理工大学学报,2020,40(8): 873-879.

    WANG Fenhua, HUANG Chao, ZHAO Bo, et al. Gesture recognition based on YOLO algorithm[J]. Transactions of Beijing Institute of Technology, 2020, 40(8): 873-879.
    [13] 昝珊珊,李波. 融合改进YOLOv2网络的视觉多目标跟踪方法[J]. 小型微型计算机系统,2020,41(12): 2601-2606.

    ZAN Shanshan, LI Bo. Visual multi-target tracking method combined with improved YOLOv2 network[J]. Journal of Chinese Computer Systems, 2020, 41(12): 2601-2606.
    [14] 寇大磊,权冀川,张仲伟. 基于深度学习的目标检测框架进展研究[J]. 计算机工程与应用,2019,55(11): 25-34.

    KOU Dalei, QUAN Jichuan, ZHANG Zhongwei. Research on progress of object detection framework based on deep learning[J]. Computer Engineering and Applications, 2019, 55(11): 25-34.
    [15] 郭璠,张泳祥,唐琎,等. YOLOv3-A:基于注意力机制的交通标志检测网络[J]. 通信学报,2021,42(1): 87-99.

    GUO Fan, ZHANG Yongyang, TANG Jin, et al. YOLOv3-A: a traffic sign detection network based on attention mechanism[J]. Journal on Communications, 2021, 42(1): 87-99.
    [16] DEGADWALA S, VYAS D, CHAKRABORTY U, et al. Yolo-v4 deep learning model for medical face mask detection[C]//2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). Coimbatore: IEEE, 2021: 209-213.
    [17] 窦其龙,颜明重,朱大奇. 基于YOLO-v5的星载SAR图像海洋小目标检测[J]. 应用科技,2021,48(6): 1-7. doi: 10.11991/yykj.202105023

    DOU Qilong, YAN Mingzhong, ZHU Daqi. Small marine target detection in space-borne SAR image based on YOLO-v5[J]. Applied Science and Technology, 2021, 48(6): 1-7. doi: 10.11991/yykj.202105023
    [18] 来文豪,周孟然,胡锋,等. 基于多光谱成像和改进YOLO v4的煤矸石检测[J]. 光学学报,2020,40(24): 72-80.

    LAI Wenhao, ZHOU Mengran, HU Feng, et al. Coal gangue detection based on multi-spectral imaging and improved YOLO v4[J]. Acta Optica Sinica, 2020, 40(24): 72-80.
    [19] KANNADAGULI P. YOLO v4 based human detection system using aerial thermal imaging for UAV based surveillance applications[C]//2020 International Conference on Decision Aid Sciences and Application (DASA). Sakheer: IEEE, 2020: 1213-1219.
    [20] DENG B Y, LEI X C, YE M. Safety helmet detection method based on YOLO v4[C]//2020 16th Inter-national Conference on Computational Intelligence and Security (CIS). Guangxi: IEEE, 2021: 155-158.
    [21] LI Y T, HUANG H S, XIE Q S, et al. Research on a surface defect detection algorithm based on MobileNet-SSD[J]. Applied Sciences, 2018, 8(9): 1678.1-1678.17. doi: 10.3390/app8091678
    [22] SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 4510-4520.
    [23] HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2020: 1314-1324.
    [24] 盛敏,刘双庆,王婕,等. 基于改进模板匹配的智能下肢假肢运动意图实时识别[J]. 控制与决策,2020,35(9): 2153-2161.

    SHENG Min, LIU Shuangqing, WANG Jie, et al. Real-time motion intent recognition of intelligent lower limb prosthesis based on improved template matching technique[J]. Control and Decision, 2020, 35(9): 2153-2161.
    [25] 刘宇晴,王天昊,徐旭. 深度学习神经网络的新型自适应激活函数[J]. 吉林大学学报(理学版),2019,57(4): 857-859.

    LIU Yuqing, WANG Tianhao, XU Xu. New adaptive activation function for deep learning neural networks[J]. Journal of Jilin University (Science Edition), 2019, 57(4): 857-859.
    [26] 马小陆,方洋,王兵,等. 一种改进的YOLO v3红外图像行人检测方法[J]. 湖北理工学院学报,2020,36(6): 19-24,38. doi: 10.3969/j.issn.2095-4565.2020.06.005

    MA Xiaolu, FANG Yang, WANG Bing, et al. An improved YOLO v3 infrared image pedestrian detection method[J]. Journal of Hubei Polytechnic University, 2020, 36(6): 19-24,38. doi: 10.3969/j.issn.2095-4565.2020.06.005
    [27] 杨蜀秦, 刘江川, 徐可可, 等. 基于改进CenterNet的玉米雄蕊无人机遥感图像识别[J]. 农业机械学报, 2021, 52(9): 206-212.

    YANG Shuqin, LIU Jiangchuan, XU Keke, et al. Improved CenterNet based maize tassel recognition for UAV remote sensing image[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(9): 206-212.
    [28] 王钏文,王磊,黄仁欢,等. 基于YOLOv3算法的中低速列车在途障碍物检测方法[J]. 铁路通信信号工程技术,2021,18(7): 86-89.

    WANG Chuanwen, WANG Lei, HUANG Renhuan, et al. Detection method of obstacles of medium-low speed train in transit based on YOLOv3 algorithm[J]. Railway Signalling & Communication Engineering, 2021, 18(7): 86-89.
    [29] 张欣,张永强,何斌,等. 基于YOLOv4-tiny的遥感图像飞机目标检测技术研究[J]. 光学技术,2021,47(3): 344-351.

    ZHANG Xin, ZHANG Yongqiang, HE Bin, et al. Research on remote sensing image aircraft target detection technology based on YOLOv4-tiny[J]. Optical Technique, 2021, 47(3): 344-351.
  • 加载中
图(14) / 表(6)
计量
  • 文章访问数:  458
  • HTML全文浏览量:  229
  • PDF下载量:  116
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-07-08
  • 修回日期:  2021-09-30
  • 网络出版日期:  2023-08-08
  • 刊出日期:  2021-10-27

目录

    /

    返回文章
    返回