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Cited by 683Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionDomain Adaptation and Few-Shot Learning

Towards Large-Scale Small Object Detection: Survey and Benchmarks

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Cheng, Gong, Yuan, Xiang, Yao, Xiwen, Yan, Kebing, Zeng, Qinghua, Xie, Xingxing, Han, Junwei·2023·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2023.3290594
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Abstract

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field.

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  • SODA includes two large-scale datasets designed for small object detection: SODA-D (autonomous driving) and SODA-A (aerial imagery).

    “SODA. To further evaluate the generalization of our framework in specialized visual domains, we adopt the SODA (Cheng et al., 2023) benchmark, which includes two large-scale datasets designed for small object detection: SODA-D (autonomous driving) and SODA-A (aerial imagery).”

    From ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning (2025) · cites this paper as Cheng et al., 2023

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Cited by

Sorted by citations

Authors
Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., & Han, J.
Affiliations
Northwestern Polytechnical University (CN)
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
45
Issue
11
Pages
1-20
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
DOI
10.1109/tpami.2023.3290594
OpenAlex
W4382568144
Cited by
683
References
29
Citations by year
2026: 141 · 2025: 260 · 2024: 207 · 2023: 69 · 2022: 6
FWCI
61.45
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
small object detection, benchmark dataset, soft drinks, aerial images, driving scenarios, large-scale datasets

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