1 code implementation • 26 Dec 2022 • Xingxing Xie, Gong Cheng, Qingyang Li, Shicheng Miao, Ke Li, Junwei Han
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not.
no code implementations • 28 Jul 2022 • Gong Cheng, Xiang Yuan, Xiwen Yao, Kebing Yan, Qinghua Zeng, Xingxing Xie, Junwei Han
Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively.
1 code implementation • 5 Oct 2021 • Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang, Yanqing Yao, Junwei Han
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
4 code implementations • ICCV 2021 • Xingxing Xie, Gong Cheng, Jiabao Wang, Xiwen Yao, Junwei Han
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
Ranked #10 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 3 May 2020 • Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo, Gui-Song Xia
Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers.