Knowledge Commons of Institute of Automation,CAS
Efficient Parallel Branch Network With Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation | |
Gao, Zishu1,2![]() ![]() ![]() ![]() | |
发表期刊 | IEEE SENSORS JOURNAL
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ISSN | 1530-437X |
2021-05-15 | |
卷号 | 21期号:10页码:12220-12227 |
摘要 | Image-based segmentation of overhead power lines is critical for power line inspection. Real-time segmentation helps the inspection robot avoid obstacles or land on the wire during the inspection task. It is challenging for several studies to achieve real-time overhead power line segmentation with high accuracy. In addition, cluttered background brings great difficulties to overhead power lines segmentation. To address these issues, an efficient parallel branch network for real-time overhead power line segmentation is proposed. Our framework combines a context branch that generates useful global information with a spatial branch that preserves high-resolution segmentation details. The asymmetric factorized depth-wise bottleneck (AFDB) module is designed in the context branch to achieve more efficient short-range feature extraction and provide a large receptive field. Furthermore, the subnetwork-level skip connections in the classifier are proposed to fuse long-range features and lead to high accuracy. Experiments demonstrate that our framework achieves more than 90% segmentation accuracy. |
关键词 | Convolution Feature extraction Image segmentation Inspection Wires Sensors Real-time systems Real-time segmentation lightweight network dilated depth-wise convolution power line inspection |
DOI | 10.1109/JSEN.2021.3062660 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018YFB1307400] ; National Natural Science Foundation[U1713224] ; National Natural Science Foundation[61973300] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation |
WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied |
WOS记录号 | WOS:000642012400099 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44531 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Yang, Guodong |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.State Grid Shandong Elect Power Co, Jinan 250001, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gao, Zishu,Yang, Guodong,Li, En,et al. Efficient Parallel Branch Network With Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation[J]. IEEE SENSORS JOURNAL,2021,21(10):12220-12227. |
APA | Gao, Zishu,Yang, Guodong,Li, En,Liang, Zize,&Guo, Rui.(2021).Efficient Parallel Branch Network With Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation.IEEE SENSORS JOURNAL,21(10),12220-12227. |
MLA | Gao, Zishu,et al."Efficient Parallel Branch Network With Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation".IEEE SENSORS JOURNAL 21.10(2021):12220-12227. |
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