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ARXIV:2603.15497 · OBJECT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15497OBJECT DETECTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images.
Opportunity summary
Pain A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear…
Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles, leading…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Notably, our O2-DFINE-L, O2-RTDETR-R50 and O2-DEIM-R50 achieve 77.73%/78.45%/80.15% AP50 on DOTA1.0 and 132/119/119 FPS on the 2080ti GPU.
Object Detection moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images.
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10.48550/arXiv.2603.15497A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images.
Abstract
Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles, leading to challenges in angle representation, matching cost, and training stability. In this paper, we propose a real-time oriented object detection transformer, the first real-time end-to-end oriented object detector to the best of our knowledge, that addresses the above issues. Specifically, angle distribution refinement is proposed to reformulate angle regression as an iterative refinement of probability distributions, thereby capturing the uncertainty of object rotation and providing a more fine-grained angle representation. Then, we incorporate a Chamfer distance cost into bipartite matching, measuring box distance via vertex sets, enabling more accurate geometric alignment and eliminating ambiguous matches. Moreover, we propose oriented contrastive denoising to stabilize training and analyze four noise modes. We observe that a ground truth can be assigned to different index queries across different decoder layers, and analyze this issue using the proposed instability metric. We design a series of model variants and experiments to validate the proposed method. Notably, our O2-DFINE-L, O2-RTDETR-R50 and O2-DEIM-R50 achieve 77.73%/78.45%/80.15% AP50 on DOTA1.0 and 132/119/119 FPS on the 2080ti GPU. Code is available at https://github.com/wokaikaixinxin/ai4rs.
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unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 8.0
PROBLEM
A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles,...
METHOD
Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles, leading to challenges in angle...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Notably, our O2-DFINE-L, O2-RTDETR-R50 and O2-DEIM-R50 achieve 77.73%/78.45%/80.15% AP50 on DOTA1.0 and 132/119/119 FPS on the 2080ti GPU.
WHY NOW
Object Detection moved forward this cycle; last verified April 2026. Public score 8.0/10.
the first real-time end-to-end oriented object detector to the best of our knowledge
Explicitly stated in the abstract as 'the first real-time end-to-end oriented object detector to the best of our knowledge'
partial
angle distribution refinement is proposed to reformulate angle regression as an iterative refinement of probability distributions, thereby capturing the uncertainty of object rotation
Directly described in the abstract as a specific technical contribution
partial
we incorporate a Chamfer distance cost into bipartite matching, measuring box distance via vertex sets, enabling more accurate geometric alignment
Directly stated in the abstract as a specific technical improvement
partial
our O2-DFINE-L, O2-RTDETR-R50 and O2-DEIM-R50 achieve 77.73%/78.45%/80.15% AP50 on DOTA1.0
Explicit numeric result provided in the abstract with specific model variant
partial
132/119/119 FPS on the 2080ti GPU
Explicit numeric performance metric provided in the abstract
partial
addresses the above issues [angle representation, matching cost, and training stability]
Directly stated as the problem being addressed, though specific improvements are described separately
partial
we propose oriented contrastive denoising to stabilize training and analyze four noise modes
Directly stated as a specific technical contribution in the abstract
partial
these detectors do not explicitly model object rotation, especially in remote sensing imagery where objects appear at arbitrary angles
Directly stated as the motivation and problem statement in the abstract
partial
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A real-time oriented object detection transformer that improves angle representation and training stability for remote sensing images.
Segment
Object Detection
Adoption evidence
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Commercial read
8.0/10 public viability
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proof status
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next verification path
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Technical feasibility
partial
Current read
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missing
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ARTIFACTS
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DEFENSIBILITY
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