  Arbitrary-oriented object detection is a relatively emerging but challenging
task. Although remarkable progress has been made, there still remain many
unsolved issues due to the large diversity of patterns in orientation, scale,
aspect ratio, and visual appearance of objects in aerial images. Most of the
existing methods adopt a coarse-grained fixed label assignment strategy and
suffer from the inconsistency between the classification score and localization
accuracy. First, to align the metric inconsistency between sample selection and
regression loss calculation caused by fixed IoU strategy, we introduce affine
transformation to evaluate the quality of samples and propose a distance-based
label assignment strategy. The proposed metric-aligned selection (MAS) strategy
can dynamically select samples according to the shape and rotation
characteristic of objects. Second, to further address the inconsistency between
classification and localization, we propose a critical feature sampling (CFS)
module, which performs localization refinement on the sampling location for
classification task to extract critical features accurately. Third, we present
a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high
quality samples by changing the form of regression loss function based on the
statistics of proposals during training. Extensive experiments are conducted on
four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016,
and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed
detector.
