  Benefiting from the great success of deep learning in computer vision,
CNN-based object detection methods have drawn significant attentions. Various
frameworks have been proposed which show awesome and robust performance for a
large range of datasets. However, for building detection in remote sensing
images, buildings always pose a diversity of orientations which makes it a
challenge for the application of off-the-shelf methods to building detection.
In this work, we aim to integrate orientation regression into the popular
axis-aligned bounding-box detection method to tackle this problem. To adapt the
axis-aligned bounding boxes to arbitrarily orientated ones, we also develop an
algorithm to estimate the Intersection over Union (IoU) overlap between any two
arbitrarily oriented boxes which is convenient to implement in Graphics
Processing Unit (GPU) for accelerating computation. The proposed method
utilizes CNN for both robust feature extraction and rotated bounding box
regression. We present our modelin an end-to-end fashion making it easy to
train. The model is formulated and trained to predict orientation, location and
extent simultaneously obtaining tighter bounding box and hence, higher mean
average precision (mAP). Experiments on remote sensing images of different
scales shows a promising performance over the conventional one.
