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

doi:10.1016/j.eswa.2022.116771

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	PLE-Net: Automatic power line extraction method using deep learning from aerial images
	Yang, Lei 2,3; Fan, Junfeng 1; Huo, Benyan 2,3; Li, En 1; Liu, Yanhong 2,3
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2022-07-15
卷号	198 页码:9
关键词	Power line extraction Deep architecture Image segmentation Multi-scale attention module
ISSN号	0957-4174
DOI	10.1016/j.eswa.2022.116771
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
英文摘要	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.
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]
WOS关键词	NETWORK
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000792808100002
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49374]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Liu, Yanhong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, 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.