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AI City Challenge 2026: Frozen detector achieves high cross-city object detection AP

By PulseAugur Editorial · [1 sources] ·

A study for the AI City Challenge 2026 explored cross-city object detection using a frozen RF-DETR-Large detector. The research found that inferring at a higher resolution (1120x1120) with frozen parameters, trained at 704x704, yielded the best aggregate AP score among tested configurations. This approach showed significant gains, particularly for small and medium-sized objects, without any parameter updates. AI

IMPACT This research demonstrates a method for improving object detection generalization across different urban environments without retraining.

RANK_REASON Academic paper detailing a study for a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI City Challenge 2026: Frozen detector achieves high cross-city object detection AP

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Academic paper detailing a study for a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jaeuk Kim ·

    Frozen High-Resolution Inference for Cross-City Object Detection: An AI City Challenge 2026 Study

    arXiv:2608.03136v1 Announce Type: new Abstract: Cross-city object detection requires a detector trained in one city to generalize to an unlabeled target city. In AI City Challenge 2026 Track 6, we analyze archived configurations of a single RF-DETR-Large detector inside an air-ga…

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