高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度学习的绝缘子定向识别算法

李彩林 张青华 陈文贺 江晓斌 袁斌 杨长磊

李彩林, 张青华, 陈文贺, 江晓斌, 袁斌, 杨长磊. 基于深度学习的绝缘子定向识别算法[J]. 电子与信息学报, 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350
引用本文: 李彩林, 张青华, 陈文贺, 江晓斌, 袁斌, 杨长磊. 基于深度学习的绝缘子定向识别算法[J]. 电子与信息学报, 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350
Cailin LI, Qinghua ZHANG, Wenhe CHEN, Xiaobin JIANG, Bin YUAN, Changlei YANG. Insulator Orientation Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350
Citation: Cailin LI, Qinghua ZHANG, Wenhe CHEN, Xiaobin JIANG, Bin YUAN, Changlei YANG. Insulator Orientation Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350

基于深度学习的绝缘子定向识别算法

doi: 10.11999/JEIT190350
基金项目: 国家自然科学基金(41601496, 41701525),山东省重点研发计划(2018GGX106002),山东省自然科学基金(ZR2017LD002),山东理工大学齐文化研究专项(2017QWH032)
详细信息
    作者简介:

    李彩林:男,1985年生,副教授,研究方向为数字摄影测量与计算机视觉

    张青华:男,1992年生,硕士,研究方向为深度学习目标检测、计算机视觉

    陈文贺:男,1992年生,硕士,研究方向为深度学习目标识别

    江晓斌:男,1994年生,硕士,研究方向为点云3维重建

    袁斌:女,1995年生,硕士,研究方向为倾斜摄影测量

    杨长磊:男,1995年生,硕士,研究方向为深度学习在农业遥感中的应用

    通讯作者:

    张青华 zhangqinghuamail@163.com

  • 中图分类号: TP391.4

Insulator Orientation Detection Based on Deep Learning

Funds: The National Naturals Science Foundation of China (41601496, 41701525), The Shandong Key R&D Program (2018GGX106002), The Shandong Natural Science Foundation (ZR2017LD002), The Qi Culture Research Project of Shandong University of Technology (2017QWH032)
  • 摘要:

    为了解决绝缘子目标检测中无法精确定位的问题,该文基于深度学习提出一种绝缘子定向识别算法,通过在轴对齐检测框中加入角度信息,可有效解决常规深度学习算法无法精确定位目标的问题。该算法首先将角度旋转参数引入轴对齐矩形检测框中构成定向检测框,然后将该参数偏移量作为第5参数加入到损失函数中进行迭代回归,同时为提高检测精度在训练过程中使用Adam算法替代随机梯度下降(SGD)算法进行损失函数优化,最终可获得绝缘子定向检测模型。实验分析表明,加入旋转角度的定向检测框可有效对绝缘子目标进行精确定位。

  • 图  1  绝缘子定向识别网络结构

    图  2  VGG-16基础网络结构图

    图  3  绝缘子定向识别算法训练流程图

    图  4  旋转角定义示意图

    图  5  轴对齐矩形框交并集示意图

    图  6  倾斜矩形框转化示意图

    图  7  训练损失曲线图

    图  8  测试影像 P-R 曲线图

    图  9  绝缘子定向识别算法测试结果图

    图  10  原始SSD轴对齐矩形框缺点

    图  11  定向矩形框优点

    图  12  扩展目标检测结果

    表  1  训练参数设定

    参数名称参数值
    初始学习率0.0001
    学习率策略Multistep
    批处理大小2
    最大时期次数100
    每期迭代次数1000
    步长值60, 80, 100
    下载: 导出CSV

    表  2  方法AP对比

    SSD模型(算法)损失函数优化方法AP
    SSD300SGD0.561
    SSD300Adam0.674
    SSD512SGD0.736
    SSD512Adam0.815
    文献[16]算法0.761
    下载: 导出CSV
  • BARTON J P and INFIELD D G. Energy storage and its use with intermittent renewable energy[J]. IEEE Transactions on Energy Conversion, 2004, 19(2): 441–448. doi: 10.1109/tec.2003.822305
    WANG Jiafu, LIANG Xidong, and GAO Yanfeng. Failure analysis of decay-like fracture of composite insulator[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2014, 21(6): 2503–2511. doi: 10.1109/tdei.2014.004485
    GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recogni-tion, Columbus, USA, 2014: 580–587.
    GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 1440–1448. doi: 10.1109/ICCV.2015.169.
    REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. The 28th Interna-tional Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91–99.
    HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988. doi: 10.1109/ICCV.2017.322.
    REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788. doi: 10.1109/CVPR.2016.91.
    REDMON J and FARHADI A. YOLO9000: Better, faster, stronger[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 6517–6525.
    REDMON J and FARHADI A. Yolov3: An incremental improvement[J]. arXiv:1804.02767, 2018.
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, the Netherlands, 2016: 21–37. doi: 10.1007/978-3-319-46448-0_2.
    FU Chengyang, LIU Wei, RANGA A, et al. DSSD: Deconvolutional single shot detector[J]. arXiv:1701.06659, 2017.
    陈庆, 闫斌, 叶润, 等. 航拍绝缘子卷积神经网络检测及自爆识别研究[J]. 电子测量与仪器学报, 2017, 31(6): 942–953. doi: 10.13382/j.jemi.2017.06.018

    CHEN Qing, YAN Bin, YE Run, et al. Insulator detection and recognition of explosion fault based on convolutional neural networks[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(6): 942–953. doi: 10.13382/j.jemi.2017.06.018
    LING Zenan, QIU R C, JIN Zhijian, et al. An accurate and real-time self-blast glass insulator location method based on faster R-CNN and U-net with aerial images[J]. arXiv:1801.05143, 2018.
    RONNEBERGER O, FISCHER P, and BROX T. U-net: Convolutional networks for biomedical image segmentation[C]. The 18th International Conference on Medical Image Computing and Computer-assisted Intervention, Munich, Germany, 2015: 234–241.
    ZHAO Zhenbing, FAN Xiaoqing, XU Guozhi, et al. Aggregating deep convolutional feature maps for insulator detection in infrared images[J]. IEEE Access, 2017, 5: 21831–21839. doi: 10.1109/ACCESS.2017.2757030
    陈文贺, 李彩林, 袁斌, 等. 有效的绝缘子自爆缺陷定位方法[J]. 计算机工程与设计, 2019, 40(8): 2346–2352. doi: 10.16208/j.issn1000-7024.2019.08.041

    CHEN Wenhe, LI Cailin, YUAN Bin, et al. Effective method to locate the self-explosion defect of insulators[J]. Computer Engineering and Design, 2019, 40(8): 2346–2352. doi: 10.16208/j.issn1000-7024.2019.08.041
    黄新波, 刘新慧, 张烨, 等. 基于红蓝色差和改进K-means算法的航拍绝缘子分类识别方法[J]. 高电压技术, 2018, 44(5): 1528–1534. doi: 10.13336/j.1003-6520.hve.20180430018

    HUANG Xinbo, LIU Xinhui, ZHANG Ye, et al. Classi-fication recognition method of insulator in aerial image based on the red-blue difference and developed K-means algorithm[J]. High Voltage Engineering, 2018, 44(5): 1528–1534. doi: 10.13336/j.1003-6520.hve.20180430018
    CHEN Chaoyue, GONG Weiguo, HU Yan, et al. Learning oriented region-based convolutional neural networks for building detection in satellite remote sensing images[C]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Han-nover, Germany, 2017: 461–464. doi: 10.5194/isprs-archives-XLII-1-W1-461-2017.
    DENG Zhipeng, SUN Hao, ZHOU Shilin, et al. Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3652–3664. doi: 10.1109/JSTARS.2017.2694890
    HE Tong, HUANG Weilin, QIAO Yu, et al. Accurate text localization in natural image with cascaded convolutional text network[J]. arXiv:1603.09423, 2016.
    YAO Cong, BAI Xiang, SANG Nong, et al. Scene text detection via holistic, multi-channel prediction[J]. arXiv:1606.09002, 2016.
    ZHANG Zheng, ZHANG Chengquan, SHEN Wei, et al. Multi-oriented text detection with fully convolutional networks[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 4159–4167. doi: 10.1109/CVPR.2016.451.
    LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 3431–3440. doi: 10.1109/CVPR.2015.7298965.
    MA Jianqi, SHAO Weiyuan, Ye Hao, et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Transactions on Multimedia, 2018, 20(11): 3111–3122. doi: 10.1109/TMM.2018.2818020
    TANG Tianyu, ZHOU Shilin, DENG Zhipeng, et al. Arbitrary-oriented vehicle detection in aerial imagery with single convolutional neural networks[J]. Remote Sensing, 2017, 9(11): 1170–1186. doi: 10.3390/rs9111170
    LIU Lei, PAN Zongxu, LEI Bin. Learning a rotation invariant detector with rotatable bounding box[J]. arXiv:1711.09405, 2017.
    ZHAO Zhenbing, LIU Ning, and WANG Le. Localization of multiple insulators by orientation angle detection and binary shape prior knowledge[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015, 22(6): 3421–3428. doi: 10.1109/tdei.2015.004741
    KINGMA D P and BA J. Adam: A method for stochastic optimization[J]. arXiv:1412.6980, 2014.
    DAI Jifeng, LI Yi, HE Kaiming, et al. R-FCN: Object detection via region-based fully convolutional net-works[C]. The 30th International Con-ference on Neural Information Processing systems, Bar-celona, Spain, 2016: 379–387.
  • 加载中
图(12) / 表(2)
计量
  • 文章访问数:  3261
  • HTML全文浏览量:  1180
  • PDF下载量:  180
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-05-17
  • 修回日期:  2019-12-02
  • 网络出版日期:  2019-12-10
  • 刊出日期:  2020-06-04

目录

    /

    返回文章
    返回