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• W4386432296 abstract "In aerial scenes, oriented object detection is sensitive to the orientation of objects, which makes the formulation of orientation-aware object representation become a critical problem. Existing methods mostly adopt rectangle anchor or discrete points as object representation, which may lead to the feature aliasing between overlapping objects and ignore the orientation information of objects. To solve these issues, we propose a novel anchor-free oriented object detection network named DRDet, which adopts Dual-angle Rotated Lines (DRL) as object representation. Different from other object representations, DRL can adaptively rotate and extend to the boundary of the object according to its orientation and shape, which explicitly introduces the orientation information into the formulation of object representation. And it can adaptively cope with the geometric deformation of objects. Based on the dual-angle rotated lines, we design an Orientation-guided Feature Encoder (OFE) to encode discriminant object feature along each rotated line, respectively. Instead of encoding rectangle feature, the OFE module adopts line features for orientation-guided feature encoding, which can alleviate the feature aliasing between neighboring objects or background. To further enhance the flexibility of dual-angle rotated lines, we design a Dual-angle Decoder (DD) that predicts two angle offsets according to the orientation-guided feature and converts the angle offsets and regression offsets into dual-angle rotated line representation, which can help to guide the adaptive rotation of each rotated line, respectively. Our proposed method achieves consistent improvement on both DOTA and HRSC2016 datasets. Extensive experimental results verify the effectiveness of our method in oriented object detection." @default.
• W4386432296 created "2023-09-05" @default.
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• W4386432296 date "2023-01-01" @default.
• W4386432296 modified "2023-10-09" @default.
• W4386432296 title "DRDet: Dual-angle Rotated Line Representation for Oriented Object Detection" @default.
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• W4386432296 doi "https://doi.org/10.1109/tgrs.2023.3311870" @default.
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