Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning

• URL: http://arxiv.org/abs/2412.11582v1
• Date: Mon, 16 Dec 2024 09:14:32 GMT
• Title: Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
• Authors: Chang Xu, Ruixiang Zhang, Wen Yang, Haoran Zhu, Fang Xu, Jian Ding, Gui-Song Xia,
• Abstract summary: We 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. We present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches.
• Score: 51.170479006249195
• License:
• 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/.

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