  Arbitrary-oriented object detection (AOOD) has been widely applied to locate
and classify objects with diverse orientations in remote sensing images.
However, the inconsistent features for the localization and classification
tasks in AOOD models may lead to ambiguity and low-quality object predictions,
which constrains the detection performance. In this article, an AOOD method
called task-wise sampling convolutions (TS-Conv) is proposed. TS-Conv
adaptively samples task-wise features from respective sensitive regions and
maps these features together in alignment to guide a dynamic label assignment
for better predictions. Specifically, sampling positions of the localization
convolution in TS-Conv are supervised by the oriented bounding box (OBB)
prediction associated with spatial coordinates, while sampling positions and
convolutional kernel of the classification convolution are designed to be
adaptively adjusted according to different orientations for improving the
orientation robustness of features. Furthermore, a dynamic
task-consistent-aware label assignment (DTLA) strategy is developed to select
optimal candidate positions and assign labels dynamically according to ranked
task-aware scores obtained from TS-Conv. Extensive experiments on several
public datasets covering multiple scenes, multimodal images, and multiple
categories of objects demonstrate the effectiveness, scalability, and superior
performance of the proposed TS-Conv.
