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基于深度學(xué)習(xí)的絕緣子定向識別算法

李彩林,  張青華,  陳文賀,  江曉斌,  袁斌,  楊長磊

李彩林, 張青華, 陳文賀, 江曉斌, 袁斌, 楊長磊. 基于深度學(xué)習(xí)的絕緣子定向識別算法[J]. 電子與信息學(xué)報, 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, 楊長磊. 基于深度學(xué)習(xí)的絕緣子定向識別算法[J]. 電子與信息學(xué)報, 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)的絕緣子定向識別算法

doi: 10.11999/JEIT190350 cstr: 32379.14.JEIT190350

  • 山東理工大學(xué)建筑工程學(xué)院 淄博 255000

基金項目: 國家自然科學(xué)基金(41601496, 41701525)，山東省重點研發(fā)計劃(2018GGX106002)，山東省自然科學(xué)基金(ZR2017LD002)，山東理工大學(xué)齊文化研究專項(2017QWH032)
詳細信息
    作者簡介:

    李彩林：男，1985年生，副教授，研究方向為數(shù)字攝影測量與計算機視覺

    張青華：男，1992年生，碩士，研究方向為深度學(xué)習(xí)目標檢測、計算機視覺

    陳文賀：男，1992年生，碩士，研究方向為深度學(xué)習(xí)目標識別

    江曉斌：男，1994年生，碩士，研究方向為點云3維重建

    袁斌：女，1995年生，碩士，研究方向為傾斜攝影測量

    楊長磊：男，1995年生，碩士，研究方向為深度學(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

  • 中圖分類號: TP391.4

Insulator Orientation Detection Based on Deep Learning

  • Shandong University of Technology, Zibo 255000, China

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檢測中無法精確定位的問題，該文基于深度學(xué)習(xí)提出一種絕緣子定向識別算法，通過在軸對齊檢測框中加入角度信息，可有效解決常規(guī)深度學(xué)習(xí)算法無法精確定位目標的問題。該算法首先將角度旋轉(zhuǎn)參數(shù)引入軸對齊矩形檢測框中構(gòu)成定向檢測框，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)中進行迭代回歸，同時為提高檢測精度在訓(xùn)練過程中使用Adam算法替代隨機梯度下降(SGD)算法進行損失函數(shù)優(yōu)化，最終可獲得絕緣子定向檢測模型。實驗分析表明，加入旋轉(zhuǎn)角度的定向檢測框可有效對絕緣子目標進行精確定位。

    Abstract:

    In order to solve the problem of inaccurate location in insulator target detection, this paper proposes an insulator orientation recognition algorithm based on deep learning. By adding angle information to the axis alignment detection frame, it can effectively solve the problem that conventional deep learning algorithm can not accurately locate the target. First, the angular rotation parameters are introduced into the axially aligned rectangular detection frame to form a directional detection frame. Then the parameter offset is added as the fifth parameter to the loss function for iterative regression. At the same time, in order to improve the detection accuracy, Adam algorithm is used to replace Stochastic Gradient Descent (SGD) to optimize the loss function. Finally, the insulator directional detection model can be obtained. The experimental results show that the orientation detection frame with rotation angle can effectively locate the insulator target accurately.

    • HTML全文

  • 圖  1  絕緣子定向識別網(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  絕緣子定向識別算法訓(xùn)練流程圖

    圖  4  旋轉(zhuǎn)角定義示意圖

    圖  5  軸對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  測試影像 P-R 曲線圖

    圖  9  絕緣子定向識別算法測試結(jié)果圖

    圖  10  原始SSD軸對齊矩形框缺點

    圖  11  定向矩形框優(yōu)點

    圖  12  擴展目標檢測結(jié)果

    表  1  訓(xùn)練參數(shù)設(shè)定

    參數(shù)名稱參數(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
    最大時期次數(shù)100
    每期迭代次數(shù)1000
    步長值60, 80, 100
    下載: 導(dǎo)出CSV

    表  2  方法AP對比

    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[16]算法–0.761
    下載: 導(dǎo)出CSV
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出版歷程
  • 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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