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

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

山東理工大學(xué)建筑工程學(xué)院淄博 255000

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
計(jì)量
- 文章訪問(wèn)數(shù): 3339
- HTML全文瀏覽量: 1222
- PDF下載量: 181
- 被引次數(shù): 0
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

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

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。
- 定向識(shí)別 /
- 絕緣子 /
- 深度學(xué)習(xí) /
- 角度旋轉(zhuǎn)
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.
- Directional recognition /
- Insulator /
- Deep learning /
- Angle rotation

HTML全文

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

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

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圖 3 絕緣子定向識(shí)別算法訓(xùn)練流程圖

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圖 4 旋轉(zhuǎn)角定義示意圖

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圖 5 軸對(duì)齊矩形框交并集示意圖

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圖 6 傾斜矩形框轉(zhuǎn)化示意圖

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圖 7 訓(xùn)練損失曲線圖

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圖 8 測(cè)試影像 P-R 曲線圖

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圖 9 絕緣子定向識(shí)別算法測(cè)試結(jié)果圖

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圖 10 原始SSD軸對(duì)齊矩形框缺點(diǎn)

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圖 11 定向矩形框優(yōu)點(diǎn)

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圖 12 擴(kuò)展目標(biāo)檢測(cè)結(jié)果

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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

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表 2 方法AP對(duì)比

SSD模型(算法)	損失函數(shù)優(yōu)化方法	AP
SSD300	SGD	0.561
SSD300	Adam	0.674
SSD512	SGD	0.736
SSD512	Adam	0.815
文獻(xiàn)[16]算法	–	0.761

下載: 導(dǎo)出CSV

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