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Shunjun Wei
Researcher at University of Electronic Science and Technology of China
Publications - 166
Citations - 2016
Shunjun Wei is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Synthetic aperture radar & Computer science. The author has an hindex of 11, co-authored 120 publications receiving 454 citations.
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Journal ArticleDOI
HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation
TL;DR: Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID, and this work has constructed a High-Resolution SAR Images Dataset (HRSID).
Journal ArticleDOI
LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images
Tianwen Zhang,Xiaoling Zhang,Xiao Ke,Xu Zhan,Jun Shi,Shunjun Wei,Dece Pan,Jianwei Li,Hao Su,Yue Zhou,Durga Kumar +10 more
TL;DR: A Large-Scale SAR Ship detection dataset from Sentinel-1 and a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images to inspire related scholars to make extensive research into SAR ship detection methods with engineering application value.
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Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection
TL;DR: A novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN), which has great application value in real-time maritime disaster rescue and emergency military planning.
Journal ArticleDOI
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
Tianwen Zhang,Xiaoling Zhang,Jianwei Li,Xiaowo Xu,Baoyou Wang,Xu Zhan,Yanqin Xu,Xiao Ke,Tianjiao Zeng,Hao Su,Israr Ahmad,Dece Pan,Chang Liu,Yue Zhou,Jun Shi,Shunjun Wei +15 more
TL;DR: Wang et al. as discussed by the authors made an official release of SSDD based on its initial version, which is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on DL.
Journal ArticleDOI
HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery
TL;DR: Experimental results on the SAR Ship Detection Dataset (SSDD), Gaofen-SSDD and Sentinel-SS DD show that HyperLi-Net’s accuracy and speed are both superior to the other nine state-of-the-art methods.