The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Rotated region based CNN for ship detection
Abstract
The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in remote sensing images. In this paper, we embrace this observation and introduce the rotated region based CNN (RR-CNN), which can learn and accurately extract features of rotated regions and locate rotated objects precisely. RR-CNN has three important new components including a rotated region of interest (RRoI) pooling layer, a rotated bounding box regression model and a multi-task method for non-maximal suppression (NMS) between different classes. Experimental results on the public ship dataset HRSC2016 confirm that RR-CNN outperforms baselines by a large margin.
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Cites
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Self-citationRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Cited by
Learning RoI Transformer for Oriented Object Detection in Aerial Images
Ding, J., Xue, N., Long, Y., Xia, G.-S., & Lu, Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00296 · Cited by 1,666
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., & Fu, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00832 · Cited by 1,044
Align Deep Features for Oriented Object Detection
Han, J., Ding, J., Li, J., & Xia, G.-S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3062048 · Cited by 1,039
Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection
Xu, Y., Fu, M., Wang, Q., Wang, Y., Chen, K., Xia, G.-S., & Bai, X. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974745 · Cited by 1,014
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Ding, J., Xue, N., Xia, G.-S., Bai, X., Yang, W., Yang, M. Y., Belongie, S., Luo, J., Datcu, M., Pelillo, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117983 · Cited by 678
Metadata
- Authors
- Liu, Z., Hu, J., Weng, L., & Yang, Y.
- Venue
- 2017 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 900-904
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2625829240
- Cited by
- 258
- References
- 13
- FWCI
- 6.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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