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

文献类型：期刊论文

作者Yang, Lei2,3; Fan, Junfeng1; Huo, Benyan2,3; Li, En1; Liu, Yanhong2,3
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期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
DOI10.1016/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.
WOS关键词NETWORK
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研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
WOS记录号WOS:000792808100002
出版者PERGAMON-ELSEVIER SCIENCE LTD
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
源URL[http://ir.ia.ac.cn/handle/173211/49374]  
专题复杂系统管理与控制国家重点实验室_水下机器人
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.

入库方式： OAI收割

来源：自动化研究所

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