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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

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 shu
Citation:  Cailin LI, Qinghua ZHANG, Wenhe CHEN, Xiaobin JIANG, Bin YUAN, Changlei YANG. Insulator Orientation Detection Based on Deep Learning[J]. Journal of Electronics and Information Technology, 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350 shu

基于深度学习的绝缘子定向识别算法

    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
  • 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)

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。

English

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  • 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
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文章相关
  • 通讯作者:  张青华, zhangqinghuamail@163.com
  • 收稿日期:  2019-05-17
  • 录用日期:  2019-12-02
  • 网络出版日期:  2019-12-10
  • 刊出日期:  2020-04-01
通讯作者: 陈斌, bchen63@163.com
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