  Arbitrary-oriented object detection (AOOD) is a challenging task to detect
objects in the wild with arbitrary orientations and cluttered arrangements.
Existing approaches are mainly based on anchor-based boxes or dense points,
which rely on complicated hand-designed processing steps and inductive bias,
such as anchor generation, transformation, and non-maximum suppression
reasoning. Recently, the emerging transformer-based approaches view object
detection as a direct set prediction problem that effectively removes the need
for hand-designed components and inductive biases. In this paper, we propose an
Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR,
which comprises three dedicated components. More precisely, an oriented
proposal generation mechanism is proposed to explicitly generate oriented
proposals, which provides better positional priors for pooling features to
modulate the cross-attention in the transformer decoder. An adaptive oriented
proposal refinement module is introduced to extract rotation-invariant region
features and eliminate the misalignment between region features and objects.
And a rotation-aware set matching loss is used to ensure the one-to-one
matching process for direct set prediction without duplicate predictions. Our
method considerably simplifies the overall pipeline and presents a new AOOD
paradigm. Comprehensive experiments on several challenging datasets show that
our method achieves superior performance on the AOOD task.
