LAUSR

Object detection in aerial images is important for a wide range of applications. The most challenging dilemma in this task is the arbitrary orientation of objects, and many deep-learning-based methods are proposed to address this issue. In previous works on oriented object detection, the regression-based method for object localization has limited performance due to the shortage of spatial information. And the models suffer from the divergence of feature construction for object recognition and localization. In this article, we propose a novel architecture, i.e., point-based estimator to remedy these problems. To utilize the spatial information explicitly, the detector encodes an oriented object with a point-based representation and operates a fully convolutional network for point localization. To improve localization accuracy, the detector takes the manner of coarse-to-fine to lessen the quantization error in point localization. To avoid the discrepancy of feature construction, the detector decouples localization and recognition with individual pathways. In the pathway of object recognition, the instance-alignment block is involved to ensure the alignment between the feature map and oriented region. Overall, the point-based estimator can be easily embedded into the region-based detector and leads to significant improvement on oriented object detection. Extensive experiments have demonstrated the effectiveness of our point-based estimator. Compared with existing works, our method shows state-of-the-art performance on oriented object detection in aerial images.