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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
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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

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