[1]黄剑锋,王淑青,王年涛,等. 面向无人机巡检的农村输电线螺栓锈蚀检测[J].湖北工业大学学报,2022,(1):54-58.
　HUANG Jianfeng,WANG Shuqing,WANG Niantao,et al. RuRal Transmission Line Bolt Corrosion Detection Method Oriented to Drone Inspection[J].,2022,(1):54-58.
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 面向无人机巡检的农村输电线螺栓锈蚀检测()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第1期
页码:
54-58
栏目:
湖北工业大学学报
出版日期:
2022-02-28

文章信息/Info

Title:
 RuRal Transmission Line Bolt Corrosion Detection Method Oriented to Drone Inspection
文章编号:
1003-4684(2022)01-0054-05
作者:
 黄剑锋;  王淑青;  王年涛;  张鹏飞;  顿伟超;  鲁 濠
 湖北工业大学电气与电子工程学院， 湖北 武汉 430068
Author(s):
 HUANG Jianfeng;  WANG Shuqing;  WANG Niantao;  ZHANG Pengfei; DUN Weichao;  LU Hao
 School of Electrical and Electronic Engineering，Hubei Univ. of Tech.，Wuhan 430068，China
关键词:
 ; 农村输电线;  无人机图像;  深度学习;  螺栓;  锈蚀检测
Keywords:
 ; Rural transmission lines;  UAV images;  deep learning;  bolts;  corrosion detection
分类号:
TM726.2, TP391.41
文献标志码:
A
摘要:
 针对农村地区输电线路螺栓锈蚀情况严重且不易检测的问题，提出一种利用深度学习目标检测网络的螺栓锈蚀检测方法，首先自制无人机图像数据集，然后利用二阶微分锐化和暗通道去雾对螺栓图像进行锐化和去雾处理，最后使用YOLOv5网络模型进行数据集的训练与测试，相比其他网络模型检测精度更高，其平均精度均值达93.6%。结果表明，所提方法能够有效实现无人机巡检图像中螺栓部件的识别与锈蚀检测。
Abstract:
 Aiming at the problem that the bolt corrosion of transmission line in rural areas is serious and difficult to detect, a bolt corrosion detection method using deep learning target detection network is proposed. Firstly, the UAV image data set is self-made, and then the bolt image is sharpened and defogged by second-order differential sharpening and dark channel defogging, Finally, the yolov5 network model is used to train and test the data set. Compared with other network models, the detection accuracy is higher, and the average accuracy is 93.6%. The results show that the proposed method can effectively realize the recognition and corrosion detection of bolt parts in UAV patrol image.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-08-14
［第一作者］ 黄剑锋(1998-)， 男， 江苏连云港人，湖北工业大学硕士研究生，研究方向为电气设备智能识别与缺陷检测
［通信作者］ 王淑青(1969-)， 女， 河北衡水人，湖北工业大学教授，研究方向为智能检测与控制
更新日期/Last Update: 2022-02-25