Small oriented objects that represent tiny pixel-area in large-scale aerial
images are difficult to detect due to their size and orientation. Existing
oriented aerial detectors have shown promising results but are mainly focused
on orientation modeling with less regard to the size of the objects. In this
work, we proposed a method to accurately detect small oriented objects in
aerial images by enhancing the classification and regression tasks of the
oriented object detection model. We designed the Attention-Points Network
consisting of two losses: Guided-Attention Loss (GALoss) and Box-Points Loss
(BPLoss). GALoss uses an instance segmentation mask as ground-truth to learn
the attention features needed to improve the detection of small objects. These
attention features are then used to predict box points for BPLoss, which
determines the points' position relative to the target oriented bounding box.
Experimental results show the effectiveness of our Attention-Points Network on
a standard oriented aerial dataset with small object instances (DOTA-v1.5) and
on a maritime-related dataset (HRSC2016). The code is publicly available.

我们提出了一种方法，通过增强定向目标检测模型的分类和回归任务，准确地检测空中图像中的小型定向目标。我们设计了一个 Attention-Points 网络，其中包含两个损失函数：Guided-Attention Loss（GALoss）和 Box-Points Loss（BPLoss）。实验结果表明，我们的 Attention-Points 网络在标准的小型目标空中数据集（DOTA-v1.5）和海事相关数据集（HRSC2016）中具有很好的效果。代码公开可用。