  Existing anchor-base oriented object detection methods have achieved amazing
results, but these methods require some manual preset boxes, which introduces
additional hyperparameters and calculations. The existing anchor-free methods
usually have complex architectures and are not easy to deploy. Our goal is to
propose an algorithm which is simple and easy-to-deploy for aerial image
detection. In this paper, we present a one-stage anchor-free rotated object
detector (FCOSR) based on FCOS, which can be deployed on most platforms. The
FCOSR has a simple architecture consisting of only convolution layers. Our work
focuses on the label assignment strategy for the training phase. We use ellipse
center sampling method to define a suitable sampling region for oriented
bounding box (OBB). The fuzzy sample assignment strategy provides reasonable
labels for overlapping objects. To solve the insufficient sampling problem, a
multi-level sampling module is designed. These strategies allocate more
appropriate labels to training samples. Our algorithm achieves 79.25, 75.41,
and 90.15 mAP on DOTA1.0, DOTA1.5, and HRSC2016 datasets, respectively. FCOSR
demonstrates superior performance to other methods in single-scale evaluation.
We convert a lightweight FCOSR model to TensorRT format, which achieves 73.93
mAP on DOTA1.0 at a speed of 10.68 FPS on Jetson Xavier NX with single scale.
The code is available at: https://github.com/lzh420202/FCOSR
