The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
Recent advances in small object detection based on deep learning: A review
Abstract
Abstract Small object detection is a challenging problem in computer vision. It has been widely applied in defense military, transportation, industry, etc. To facilitate in-depth understanding of small object detection, we comprehensively review the existing small object detection methods based on deep learning from five aspects, including multi-scale feature learning, data augmentation, training strategy, context-based detection and GAN-based detection. Then, we thoroughly analyze the performance of some typical small object detection algorithms on popular datasets, such as MS-COCO, PASCAL-VOC. Finally, the possible research directions in the future are pointed out from five perspectives: emerging small object detection datasets and benchmarks, multi-task joint learning and optimization, information transmission, weakly supervised small object detection methods and framework for small object detection task.
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Cites
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Cited by
A survey and performance evaluation of deep learning methods for small object detection
Liu, Y., Sun, P., Wergeles, N., & Shang, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114602 · Cited by 665
Fault diagnosis for small samples based on attention mechanism
Zhang, X., He, C., Lu, Y., Chen, B., Zhu, L., & Zhang, L. (2022) · Measurement · doi:10.1016/j.measurement.2021.110242 · Cited by 299
RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring
Sun, W., Dai, L., Zhang, X., Chang, P., & He, X. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02893-3 · Cited by 281
Chen, J., Han, X., Liu, Z., Zhou, C., Hu, R., Tabassam, S., Wyatt-Johnson, S. K., Oblak, A. L., Brutkiewicz, R. R., Lin, M., et al. (2026) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3614233 · Cited by 2
“Some strategies can be used for dealing with small object detection shortcomings [67].”
Metadata
- Authors
- Tong, K., Wu, Y., & Zhou, F.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 97
- Pages
- 103910
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W3013211776
- Cited by
- 592
- References
- 107
- FWCI
- 28.62
- Type
- journal article
- Language
- en
- Open access
- closed
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