Abstract
Detecting objects in aerial images is a long-standing and challenging problem since the objects in aerial images vary dramatically in size and orientation. Most existing neural network based methods are not robust enough to provide accurate oriented object detection results in aerial images since they do not consider the correlations between different levels and scales of features. In this paper, we propose a novel two-stage network-based detector with
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Index Terms
Towards Accurate Oriented Object Detection in Aerial Images with Adaptive Multi-level Feature Fusion
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Highlights- Investigated the oriented object detection method based on deep learning.
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