TY - GEN
T1 - Dynamic Proposal Generation for Oriented Object Detection in Aerial Images
AU - Li, Qingyang
AU - Cheng, Gong
AU - Miao, Shicheng
AU - Pei, Lei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Current two-stage oriented object detectors for aerial images have achieved remarkable progress. However, they still suffer from some drawbacks. Firstly, most of them place redundant anchors or utilize complicated transformation to generate oriented proposals, which are inefficient. Secondly, the generation of proposals is static, which cannot adapt to the extremely nonuniform distribution of objects. To address these issues, we propose a Dynamic Proposal Generation Network (DPGN) which can generate high-quality oriented proposals directly and estimate the upper limit of proposals adaptively. To be specific, with Guided Anchor Regression (GAR), we obtain the coarse oriented anchors and utilize them to align the features. After this, we make further classification and regression to produce final oriented proposals. Meanwhile, we design Maximum Number Estimation (MNE) for predicting an approximate value to remain the proposals adaptively. Without tricks, our method can achieve competitive detection accuracy compared with other mainstream methods on DOTA dataset.
AB - Current two-stage oriented object detectors for aerial images have achieved remarkable progress. However, they still suffer from some drawbacks. Firstly, most of them place redundant anchors or utilize complicated transformation to generate oriented proposals, which are inefficient. Secondly, the generation of proposals is static, which cannot adapt to the extremely nonuniform distribution of objects. To address these issues, we propose a Dynamic Proposal Generation Network (DPGN) which can generate high-quality oriented proposals directly and estimate the upper limit of proposals adaptively. To be specific, with Guided Anchor Regression (GAR), we obtain the coarse oriented anchors and utilize them to align the features. After this, we make further classification and regression to produce final oriented proposals. Meanwhile, we design Maximum Number Estimation (MNE) for predicting an approximate value to remain the proposals adaptively. Without tricks, our method can achieve competitive detection accuracy compared with other mainstream methods on DOTA dataset.
KW - Aerial Images
KW - Dynamic Proposal Generation
KW - Oriented Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85141896321&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884707
DO - 10.1109/IGARSS46834.2022.9884707
M3 - 会议稿件
AN - SCOPUS:85141896321
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3107
EP - 3110
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -