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Arbitrary-oriented object detection is an important task in the field of remote sensing object detection.
B. Gergič, P. Planinšič, B. Banjanin, D. Gleich, and Ž. Čučej, “A comparison between sar data compression in cartesian and polar coordinates,” International Journal of Remote Sensing , vol. 25, no. 10, pp. 1987–1994, 2004
2004
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2010
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W. Zhang, X. Sun, K. Fu, C. Wang, and H. Wang, “Object detection in high-resolution remote sensing images using rotation invariant parts based model,” IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 1, pp. 74–78, 2013
2013
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2014
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
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W. Zhang, X. Sun, H. Wang, and K. Fu, “A generic discriminative part-based model for geospatial object detection in optical remote sensing images,” ISPRS journal of photogrammetry and remote sensing , vol. 99, pp. 30–44, 2015
2015
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R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
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H. Zhu, X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, “Orientation robust object detection in aerial images using deep convolutional neural network,” in 2015 IEEE International Conference on Image Processing (ICIP) . IEEE, 2015, pp. 3735–3739
2015
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2016
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2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
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J. Dai, Y. Li, K. He, and J. Sun, “R-fcn: Object detection via region-based fully convolutional networks,” in Advances in neural information processing systems , 2016, pp. 379–387
2016
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T. Kong, A. Yao, Y. Chen, and F. Sun, “Hypernet: Towards accurate region proposal generation and joint object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 845–853
2016
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S. Gai, F. Da, and X. Fang, “A novel camera calibration method based on polar coordinate,” Plos one , vol. 11, no. 10, p. e0165487, 2016
2016
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G. Cheng, P. Zhou, and J. Han, “Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 54, no. 12, pp. 7405–7415, 2016
2016
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A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 483–499
2016
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Cited alongside, same era.
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Cited alongside, same era.
K. Fu, W. Lu, W. Diao, M. Yan, H. Sun, Y. Zhang, and X. Sun, “Wsf-net: Weakly supervised feature-fusion network for binary segmentation in remote sensing image,” Remote Sensing , vol. 10, no. 12, p. 1970, 2018
2018
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P. Lyu, C. Yao, W. Wu, S. Yan, and X. Bai, “Multi-oriented scene text detection via corner localization and region segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7553–7563
2018
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G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3974–3983
2018
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S. M. Azimi, E. Vig, R. Bahmanyar, M. Körner, and P. Reinartz, “Towards multi-class object detection in unconstrained remote sensing imagery,” in Asian Conference on Computer Vision . Springer, 2018, pp. 150–165
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J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7263–7271
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
2017
Cited alongside, same era.
Z. Deng, H. Sun, S. Zhou, J. Zhao, L. Lei, and H. Zou, “Multi-scale object detection in remote sensing imagery with convolutional neural networks,” ISPRS journal of photogrammetry and remote sensing , vol. 145, pp. 3–22, 2018
2018
Cited alongside, same era.
P. Ding, Y. Zhang, W.-J. Deng, P. Jia, and A. Kuijper, “A light and faster regional convolutional neural network for object detection in optical remote sensing images,” ISPRS journal of photogrammetry and remote sensing , vol. 141, pp. 208–218, 2018
2018
Cited alongside, same era.
S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, “Single-shot refinement neural network for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4203–4212
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
P. Wang, X. Sun, W. Diao, and K. Fu, “Mergenet: Feature-merged network for multi-scale object detection in remote sensing images,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 238–241
2019
Later among the works it cites.
X. Yang, J. Yang, J. Yan, Y. Zhang, T. Zhang, Z. Guo, X. Sun, and K. Fu, “Scrdet: Towards more robust detection for small, cluttered and rotated objects,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 8232–8241
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Wang, Y. Zhang, Y. Zhang, L. Zhao, X. Sun, and Z. Guo, “Sard: Towards scale-aware rotated object detection in aerial imagery,” IEEE Access , vol. 7, pp. 173 855–173 865, 2019
2019
Later among the works it cites.
X. Zhou, J. Zhuo, and P. Krahenbuhl, “Bottom-up object detection by grouping extreme and center points,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 850–859
2019
Later among the works it cites.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
Later among the works it cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Proceedings of the IEEE international conference on computer vision , 2019, pp. 9627–9636
2019
Later among the works it cites.
J. Ding, N. Xue, Y. Long, G.-S. Xia, and Q. Lu, “Learning roi transformer for oriented object detection in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2849–2858
2019
Later among the works it cites.
L. Zhang, H. Guo, H. Jiao, G. Liu, G. Shen, and W. Wu, “A polar coordinate system based on a projection surface for moon-based earth observation images,” Advances in Space Research , vol. 64, no. 11, pp. 2209–2220, 2019
2019
Later among the works it cites.
K. Fu, Z. Chang, Y. Zhang, G. Xu, K. Zhang, and X. Sun, “Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 161, pp. 294–308, 2020
2020
Closest in time.
H. Wei, Y. Zhang, B. Wang, Y. Yang, H. Li, and H. Wang, “X-linenet: Detecting aircraft in remote sensing images by a pair of intersecting line segments,” IEEE Transactions on Geoscience and Remote Sensing , 2020
2020
Closest in time.