The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Open accessAbstract
Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in horizontal bounding box such that the region of interest is contaminated with background or nearby interleaved objects. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects. To handle the rotation variation, we also add a novel IoU constant factor to the smooth L1 loss to address the long standing boundary problem, which to our analysis, is mainly caused by the periodicity of angular (PoA) and exchangeability of edges (EoE). By combing these two features, our proposed detector is termed as SCRDet++. Extensive experiments are performed on large aerial images public datasets DOTA, DIOR, UCAS-AOD as well as natural image dataset COCO, scene text dataset ICDAR2015, small traffic light dataset BSTLD and our released S$^{2}$TLD by this paper. The results show the effectiveness of our approach. The released dataset S$^{2}$TLD is made public available, which contains 5,786 images with 14,130 traffic light instances across five categories.
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Microsoft COCO: Common Objects in Context
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ReDet: A Rotation-equivariant Detector for Aerial Object Detection
Han, J., Ding, J., Xue, N., & Xia, G.-S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00281 · Cited by 939
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RingMo: A Remote Sensing Foundation Model With Masked Image Modeling
Sun, X., Wang, P., Lu, W., Zhu, Z., Lu, X., He, Q., Li, J., Rong, X., Yang, Z., Chang, H., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194732 · Cited by 323
Shape-Adaptive Selection and Measurement for Oriented Object Detection
Hou, L., Lu, K., Xue, J., & Li, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19975 · Cited by 298
Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection
Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., & Ye, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00868 · Cited by 258
Metadata
- Authors
- Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., & He, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-02-01
- Volume
- 45
- Issue
- 2
- Pages
- 2384-2399
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3022917557
- Cited by
- 365
- References
- 89
- FWCI
- 24.36
- Type
- journal article
- Language
- en
- Open access
- green
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- Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., & He, T. (2023). SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2022.3166956
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