  A novel object detection method is presented that handles freely rotated
objects of arbitrary sizes, including tiny objects as small as $2\times 2$
pixels. Such tiny objects appear frequently in remotely sensed images, and
present a challenge to recent object detection algorithms. More importantly,
current object detection methods have been designed originally to accommodate
axis-aligned bounding box detection, and therefore fail to accurately localize
oriented boxes that best describe freely rotated objects. In contrast, the
proposed CNN-based approach uses potential pixel information at multiple scale
levels without the need for any external resources, such as anchor boxes.The
method encodes the precise location and orientation of features of the target
objects at grid cell locations. Unlike existing methods which regress the
bounding box location and dimension,the proposed method learns all the required
information by classification, which has the added benefit of enabling oriented
bounding box detection without any extra computation. It thus infers the
bounding boxes only at inference time by finding the minimum surrounding box
for every set of the same predicted class labels. Moreover, a
rotation-invariant feature representation is applied to each scale, which
imposes a regularization constraint to enforce covering the 360 degree range of
in-plane rotation of the training samples to share similar features.
Evaluations on the xView and DOTA datasets show that the proposed method
uniformly improves performance over existing state-of-the-art methods.
