基于深度学习的绝缘子定向识别算法  

Insulator Orientation Detection Based on Deep Learning

作　　者：李彩林[1] 张青华 陈文贺[1] 江晓斌 袁斌 杨长磊 LI Cailin;ZHANG Qinghua;CHEN Wenhe;JIANG Xiaobin;YUAN Bin;YANG Changlei(Shandong University of Technology,Zibo 255000,China)

机构地区：[1]山东理工大学建筑工程学院,淄博255000

出　　处：《电子与信息学报》2020年第4期1033-1040,共8页Journal of Electronics & Information Technology

基　　金：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。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.

关 键 词：定向识别 绝缘子 深度学习 角度旋转 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]