A light defect detection algorithm of power insulators from aerial images for power inspection

doi:10.1007/s00521-022-07437-5

	A light defect detection algorithm of power insulators from aerial images for power inspection
	Yang, Lei 1; Fan, Junfeng2 ; Song, Shouan 1; Liu, Yanhong1
发表期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
	2022-06-07
页码	11
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
摘要	With the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a common defect of power insulators, missing-cap issue will affect the structural strength and durability of different power insulators. Therefore, the condition monitoring of power insulators is a daily but priority power line inspection task. Faced with the weak image features of small insulator defects in the aerial images, the conventional handcrafted features could not extract effectively powerful image features. Meanwhile, the small-scale insulator defects will bring a certain effect to the model training of deep learning. Therefore, the high-efficiency and accurate defect inspection still present a challenging task against complex backgrounds. To address the above issues, aimed at the missing-cap defects of power insulators, a novel defect identification algorithm from aerial images is proposed by taking advantage of state-of-the-art deep learning and transfer learning models. Fused with Spatial Pyramid Pooling (SPP) and MobileNet networks, a light deep convolutional neural network (DCNN) model based on You Only Look Once (YOLO) V3 network is proposed for fast and accurate insulator location to remove complex background interference. On the basis, combined with Dempster-Shafer (DS) evidence theory, the improved transfer learning model based on feature fusion is proposed for high-precision defect identification of power insulators. Experiments show that the proposed method could acquire a better identification performance against complex power inspection environment compared with other related detection models.
关键词	Insulator location Defect identification Transfer learning Dempster-Shafer evidence theory
DOI	10.1007/s00521-022-07437-5
关键词[WOS]	FAULT-DETECTION ; CLASSIFICATION
收录类别	SCI
语种	英语
资助项目	National Key Research & Development Project of China[2020YFB1313701] ; National Natural Science Foundation of China[62003309] ; Science & Technology Research Project in Henan Province of China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008]
项目资助者	National Key Research & Development Project of China ; National Natural Science Foundation of China ; Science & Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000806702300001
出版者	SPRINGER LONDON LTD
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49516
专题	复杂系统认知与决策实验室_水下机器人
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Fan, Junfeng,Song, Shouan,et al. A light defect detection algorithm of power insulators from aerial images for power inspection[J]. NEURAL COMPUTING & APPLICATIONS,2022:11.
APA	Yang, Lei,Fan, Junfeng,Song, Shouan,&Liu, Yanhong.(2022).A light defect detection algorithm of power insulators from aerial images for power inspection.NEURAL COMPUTING & APPLICATIONS,11.
MLA	Yang, Lei,et al."A light defect detection algorithm of power insulators from aerial images for power inspection".NEURAL COMPUTING & APPLICATIONS (2022):11.