PLE-Net: Automatic power line extraction method using deep learning from aerial images

doi:10.1016/j.eswa.2022.116771

	PLE-Net: Automatic power line extraction method using deep learning from aerial images
	Yang, Lei1,2 ; Fan, Junfeng3 ; Huo, Benyan 1,2; Li, En3 ; Liu, Yanhong1,2
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
	2022-07-15
卷号	198 页码:9
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
摘要	Automatic power line extraction is a crucial task for the safe navigation of inspection robots. Nevertheless, power lines are always against complicated natural backgrounds which bring a certain challenge for accurate power line extraction. Meanwhile, the power lines always occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes serious class imbalance issue. Therefore, the robust and accurate power line segmentation from aerial images is one of the most frequently stated problems faced with these factors. Recently, the deep learning has got wide applications on different segmentation tasks with effective contextual feature generation ability. However, these methods show poor ability on the samples with class imbalance due to insufficient process of local contextual features. To address these issues, combined with the encoder-decoder framework, a novel power line extraction network (PLE-Net) is proposed in this paper to construct an end-to-end attention-based segmentation method for automatic power line extraction from aerial images with a self-attention block and a multi-scale feature enhance block. To capture rich contextual relationships from local feature maps, a feature enhance block is proposed for multi-scale feature expression. And a self-attention block is proposed to embed into the proposed segmentation network to emphasize the regions about power lines. Further, the hybrid loss function with binary cross-entropy (BCE) and Dice is set as the loss function to address the class imbalance issue. Combined with the public datasets of power lines, the proposed segmentation network shows a better segmentation performance on vision images and infrared images through the ablation analysis and comparison experiments.
关键词	Power line extraction Deep architecture Image segmentation Multi-scale attention module
DOI	10.1016/j.eswa.2022.116771
关键词[WOS]	NETWORK
收录类别	SCI
语种	英语
资助项目	National Natural Science Founda-tion of China[62003309] ; National Key Research & Devel-opment Project of China[2020YFB1313701] ; 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 Natural Science Founda-tion of China ; National Key Research & Devel-opment Project 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 ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000792808100002
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：13[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/173211/49375
专题	复杂系统认知与决策实验室_水下机器人复杂系统认知与决策实验室_先进机器人
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.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,Huo, Benyan,et al. PLE-Net: Automatic power line extraction method using deep learning from aerial images[J]. EXPERT SYSTEMS WITH APPLICATIONS,2022,198:9.
APA	Yang, Lei,Fan, Junfeng,Huo, Benyan,Li, En,&Liu, Yanhong.(2022).PLE-Net: Automatic power line extraction method using deep learning from aerial images.EXPERT SYSTEMS WITH APPLICATIONS,198,9.
MLA	Yang, Lei,et al."PLE-Net: Automatic power line extraction method using deep learning from aerial images".EXPERT SYSTEMS WITH APPLICATIONS 198(2022):9.