AO2-DETR: Arbitrary-Oriented Object Detection Transformer

• URL: http://arxiv.org/abs/2205.12785v1
• Date: Wed, 25 May 2022 13:57:13 GMT
• Title: AO2-DETR: Arbitrary-Oriented Object Detection Transformer
• Authors: Linhui Dai, Hong Liu, Hao Tang, Zhiwei Wu, Pinhao Song
• Abstract summary: We propose an Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR. An oriented proposal generation mechanism is proposed to explicitly generate oriented proposals. And a rotation-aware set matching loss is used to ensure the one-to-one matching process for direct set prediction.
• Score: 17.287517988299925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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