# Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning Source: [Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:50:30.323Z Authors: Chang Xu, Ruixiang Zhang, Wen Yang, Haoran Zhu, Fang Xu, Jian Ding, Gui-Song Xia Published: 2024-12-16T09:14:32.000Z ## Abstract Detecting oriented tiny objects, which are limited in appearance information yet prevalent in real-world applications, remains an intricate and under-explored problem. To address this, we systemically introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study. Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets. Based on AI-TOD-R, we present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches. Through investigation, we identify a learning bias presents across various learning pipelines: confident objects become increasingly confident, while vulnerable oriented tiny objects are further marginalized, hindering their detection performance. To mitigate this issue, we propose a Dynamic Coarse-to-Fine Learning (DCFL) scheme to achieve unbiased learning. DCFL dynamically updates prior positions to better align with the limited areas of oriented tiny objects, and it assigns samples in a way that balances both quantity and quality across different object shapes, thus mitigating biases in prior settings and sample selection. Extensive experiments across eight challenging object detection datasets demonstrate that DCFL achieves state-of-the-art accuracy, high efficiency, and remarkable versatility. The dataset, benchmark, and code are available at https://chasel-tsui.github.io/AI-TOD-R/. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Oriented Object Detection on SODA-A (test)](): Oriented Object Detection; SODA-A (test); AP: 36.6. ## Original sources and implementation [Original paper]() [PDF]()