Improving the Detection of Small Oriented Objects in Aerial Images
Chandler Timm C. Doloriel Rhandley D. Cajote
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.