DFIR-DETR: Frequency-Domain Iterative Refinement and Dynamic Feature Aggregation for Small Object Detection
arXiv:2512.07078v4 Announce Type: replace-cross Abstract: Small object detection in complex scenes exposes a fundamental tension in neural network design: backbone attention distributes computation uniformly regardless of content, pyramid necks inflate activation magnitudes during upsampling without norm compensation, and bottleneck convolutions progressively smooth high-frequency edge components through accumulated spatial filtering. In response, we develop DFIR-DETR by tracing each proposed module back to a specific, measurable deficiency in the RT-DETR baseline: uniform attention that ignor
This research addresses known deficiencies in current state-of-the-art small object detection models, presenting a specialized solution that leverages recent advancements in AI architecture design.
Improved small object detection is critical for numerous applications, including autonomous systems, surveillance, and medical imaging, directly impacting the reliability and capability of AI-driven systems in complex real-world scenarios.
The proposed DFIR-DETR directly tackles a long-standing challenge in computer vision by offering a more robust and efficient method for identifying small objects, potentially enabling more accurate and safer AI deployments.
- · AI/ML research community
- · Autonomous vehicle developers
- · Surveillance technology providers
- · Medical imaging software
- · Developers relying on generic object detection models
- · Legacy computer vision systems
Enhancement of AI systems reliant on precise small object recognition.
Accelerated development and deployment of autonomous systems with improved safety and performance.
New application areas for AI that were previously limited by small object detection capabilities, fostering innovation across multiple sectors.
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Read at arXiv cs.LG