TL;DR
An Adaptive Geometry-aware Network (AG-Net) tailored for oriented object detection that outperforms other state-of-the-art methods and captures long-range spatial dependencies and edge details to improve localization accuracy amidst complex backgrounds.
Experimental results on public benchmark datasets demonstrate that the proposed SMDS-Net achieves highly competitive detection accuracy while significantly reducing the number of model parameters and theoretical computational complexity.
Oriented object detection in remote sensing images plays an important role in maritime monitoring, airport surveillance, and traffic management. However, densely distributed small objects and slender-structured objects remain highly challenging to detect because they are susceptible to object adhesion, background inter...
UAV-based small object detection remains challenging because aerial targets are often densely distributed, weakly textured, partially occluded, and affected by complex backgrounds and illumination variations. To address these issues, this paper presents SAFLR-Net, a scale-aware feature enhancement and localization re...
Lightweight oriented object detection in high-resolution remote sensing imagery is challenging, since detectors must handle substantial variations in object scale and complex object geometries under strict computational constraints. Prevailing lightweight methods often focus on backbone compression, leaving the neck an...
Small object detection in aerial images confronts three intertwined challenges that span the detection pipeline: the mismatch between the fixed receptive fields of backbone networks and the extreme scale variation of targets, the joint degradation of shallow-layer representations caused by gradient attenuation and spec...
Experimental results on the VisDrone2019 dataset demonstrate that Aerial-YOLO significantly improves small object detection performance while maintaining real-time inference capability, and strong generalization ability and robustness of the proposed method are verified.
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