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基于深度學(xué)習(xí)的絕緣子定向識(shí)別算法

李彩林 張青華 陳文賀 江曉斌 袁斌 楊長(zhǎng)磊

李彩林, 張青華, 陳文賀, 江曉斌, 袁斌, 楊長(zhǎng)磊. 基于深度學(xué)習(xí)的絕緣子定向識(shí)別算法[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 1033-1040. doi: 10.11999/JEIT190350
引用本文: 李彩林, 張青華, 陳文賀, 江曉斌, 袁斌, 楊長(zhǎng)磊. 基于深度學(xué)習(xí)的絕緣子定向識(shí)別算法[J]. 電子與信息學(xué)報(bào), 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

基于深度學(xué)習(xí)的絕緣子定向識(shí)別算法

doi: 10.11999/JEIT190350
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(41601496, 41701525),山東省重點(diǎn)研發(fā)計(jì)劃(2018GGX106002),山東省自然科學(xué)基金(ZR2017LD002),山東理工大學(xué)齊文化研究專(zhuān)項(xiàng)(2017QWH032)
詳細(xì)信息
    作者簡(jiǎn)介:

    李彩林:男,1985年生,副教授,研究方向?yàn)閿?shù)字?jǐn)z影測(cè)量與計(jì)算機(jī)視覺(jué)

    張青華:男,1992年生,碩士,研究方向?yàn)樯疃葘W(xué)習(xí)目標(biāo)檢測(cè)、計(jì)算機(jī)視覺(jué)

    陳文賀:男,1992年生,碩士,研究方向?yàn)樯疃葘W(xué)習(xí)目標(biāo)識(shí)別

    江曉斌:男,1994年生,碩士,研究方向?yàn)辄c(diǎn)云3維重建

    袁斌:女,1995年生,碩士,研究方向?yàn)閮A斜攝影測(cè)量

    楊長(zhǎng)磊:男,1995年生,碩士,研究方向?yàn)樯疃葘W(xué)習(xí)在農(nóng)業(yè)遙感中的應(yīng)用

    通訊作者:

    張青華 zhangqinghuamail@163.com

  • 中圖分類(lèi)號(hào): 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:

    為了解決絕緣子目標(biāo)檢測(cè)中無(wú)法精確定位的問(wèn)題,該文基于深度學(xué)習(xí)提出一種絕緣子定向識(shí)別算法,通過(guò)在軸對(duì)齊檢測(cè)框中加入角度信息,可有效解決常規(guī)深度學(xué)習(xí)算法無(wú)法精確定位目標(biāo)的問(wèn)題。該算法首先將角度旋轉(zhuǎn)參數(shù)引入軸對(duì)齊矩形檢測(cè)框中構(gòu)成定向檢測(cè)框,然后將該參數(shù)偏移量作為第5參數(shù)加入到損失函數(shù)中進(jìn)行迭代回歸,同時(shí)為提高檢測(cè)精度在訓(xùn)練過(guò)程中使用Adam算法替代隨機(jī)梯度下降(SGD)算法進(jìn)行損失函數(shù)優(yōu)化,最終可獲得絕緣子定向檢測(cè)模型。實(shí)驗(yàn)分析表明,加入旋轉(zhuǎn)角度的定向檢測(cè)框可有效對(duì)絕緣子目標(biāo)進(jìn)行精確定位。

  • 圖  1  絕緣子定向識(shí)別網(wǎng)絡(luò)結(jié)構(gòu)

    圖  2  VGG-16基礎(chǔ)網(wǎng)絡(luò)結(jié)構(gòu)圖

    圖  3  絕緣子定向識(shí)別算法訓(xùn)練流程圖

    圖  4  旋轉(zhuǎn)角定義示意圖

    圖  5  軸對(duì)齊矩形框交并集示意圖

    圖  6  傾斜矩形框轉(zhuǎn)化示意圖

    圖  7  訓(xùn)練損失曲線圖

    圖  8  測(cè)試影像 P-R 曲線圖

    圖  9  絕緣子定向識(shí)別算法測(cè)試結(jié)果圖

    圖  10  原始SSD軸對(duì)齊矩形框缺點(diǎn)

    圖  11  定向矩形框優(yōu)點(diǎn)

    圖  12  擴(kuò)展目標(biāo)檢測(cè)結(jié)果

    表  1  訓(xùn)練參數(shù)設(shè)定

    參數(shù)名稱(chēng)參數(shù)值
    初始學(xué)習(xí)率0.0001
    學(xué)習(xí)率策略Multistep
    批處理大小2
    最大時(shí)期次數(shù)100
    每期迭代次數(shù)1000
    步長(zhǎng)值60, 80, 100
    下載: 導(dǎo)出CSV

    表  2  方法AP對(duì)比

    SSD模型(算法)損失函數(shù)優(yōu)化方法AP
    SSD300SGD0.561
    SSD300Adam0.674
    SSD512SGD0.736
    SSD512Adam0.815
    文獻(xiàn)[16]算法0.761
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2019-05-17
  • 修回日期:  2019-12-02
  • 網(wǎng)絡(luò)出版日期:  2019-12-10
  • 刊出日期:  2020-06-04

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