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With the rapid development of deep learning, many deep learning-based approaches have made great achievements in object detection task.
Mmdetection: Open mmlab detection toolbox and benchmark
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., et al., 2019 · 1906
Earlier work this paper cites.
Fast multiclass vehicle detection on aerial images
Liu, K., Mattyus, G., 2015 · 1942
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Dsod: Learning deeply supervised object detectors from scratch
Shen, Z., Liu, Z., Li, J., Jiang, Y., Chen, Y., Xue, X., 2017 · 1945
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Moving and stationary target acquisition and recognition (mstar) model-based automatic target recognition: Search technology for a robust atr, in: Algorithms for synthetic aperture radar Imagery V, International Society for Optics and Photonics. pp. 481–492
Diemunsch, J.R., Wissinger, J., 1998 · 1998
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Fgsd: A dataset for fine-grained ship detection in high resolution satellite images
Chen, K., Wu, M., Liu, J., Zhang, C., 2020 · 2003
Earlier work this paper cites.
Multi-branch and multi-scale attention learning for fine-grained visual categorization
Zhang, F., Li, M., Zhai, G., Liu, Y., 2020 · 2003
Earlier work this paper cites.
Tide: A general toolbox for identifying object detection errors
Bolya, D., Foley, S., Hays, J., Hoffman, J., 2020 · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., 2010 · 2010
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Worldview-2 pan-sharpening, in: Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA, pp. 1–14
Padwick, C., Deskevich, M., Pacifici, F., Smallwood, S., 2010 · 2010
Earlier work this paper cites.
Bag-of-visual-words and spatial extensions for land-use classification, in: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp. 270–279
Yang, Y., Newsam, S., 2010 · 2010
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Novel dataset for fine-grained image categorization, in: First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO
Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L., 2011 · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S., 2011 · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A., 2012 · 2012
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The ISPRS benchmark on urban object classification and 3D building reconstruction, in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 293–298
Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., Breitkopf, U., 2012 · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization, in: 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia
Krause, J., Stark, M., Deng, J., Fei-Fei, L., 2013 · 2013
Earlier work this paper cites.
Vehicle detection in satellite images by hybrid deep convolutional neural networks
Chen, X., Xiang, S., Liu, C., Pan, C., 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context, in: European conference on computer vision, Springer. pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Earlier work this paper cites.
Results of the isprs benchmark on urban object detection and 3d building reconstruction
Rottensteiner, F., Sohn, G., Gerke, M., Wegner, J.D., Breitkopf, U., Jung, J., 2014 · 2014
Earlier work this paper cites.
Aircraft recognition in high-resolution optical satellite remote sensing images
Wu, Q., Sun, H., Sun, X., Zhang, D., Fu, K., Wang, H., 2014 · 2014
Earlier work this paper cites.
Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning
Han, J., Zhang, D., Cheng, G., Guo, L., Ren, J., 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: towards real-time object detection with region proposal networks 2015, 91–99
Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
Earlier work this paper cites.
Elliptic fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images
Xiao, Z., Liu, Q., Tang, G., Zhai, X., 2015 · 2015
Earlier work this paper cites.
Orientation robust object detection in aerial images using deep convolutional neural network, in: 2015 IEEE International Conference on Image Processing (ICIP), IEEE. pp. 3735–3739
Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., Jiao, J., 2015 · 2015
Cited alongside, same era.
A survey on object detection in optical remote sensing images
Cheng, G., Han, J., 2016 · 2016
Cited alongside, same era.
Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images
Cheng, G., Zhou, P., Han, J., 2016 · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding, in: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
xview: Objects in context in overhead imagery
Lam, D., Kuzma, R., McGee, K., Dooley, S., Laielli, M., Klaric, M., Bulatov, Y., McCord, B., 2018 · 2018
Later among the works it cites.
Yolov3: An incremental improvement
Redmon, J., Farhadi, A., 2018 · 2018
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Deformable faster r-cnn with aggregating multi-layer features for partially occluded object detection in optical remote sensing images
Ren, Y., Zhu, C., Xiao, S., 2018 · 2018
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Multiscale visual attention networks for object detection in vhr remote sensing images
Wang, C., Bai, X., Wang, S., Zhou, J., Ren, P., 2018 · 2018
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Dota: A large-scale dataset for object detection in aerial images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983
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He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Hypernet: Towards accurate region proposal generation and joint object detection
Kong, T., Yao, A., Chen, Y., Sun, F., 2016 · 2016
Cited alongside, same era.
Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds
Liu, Z., Wang, H., Weng, L., Yang, Y., 2016 · 2016
Cited alongside, same era.
A large contextual dataset for classification, detection and counting of cars with deep learning, in: European Conference on Computer Vision, Springer. pp. 785–800
Mundhenk, T.N., Konjevod, G., Sakla, W.A., Boakye, K., 2016 · 2016
Cited alongside, same era.
Vehicle detection in aerial imagery: A small target detection benchmark
Razakarivony, S., Jurie, F., 2016 · 2016
Cited alongside, same era.
An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery
Han, X., Zhong, Y., Zhang, L., 2017 · 2017
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al., 2017 · 2017
Cited alongside, same era.
Fssd: Feature fusion single shot multibox detector
Li, Z., Zhou, F., 2017 · 2017
Cited alongside, same era.
Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L., 2018 · 2018
Later among the works it cites.
Learning roi transformer for oriented object detection in aerial images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2849–2858
Ding, J., Xue, N., Long, Y., Xia, G.S., Lu, Q., 2019 · 2019
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Objects365: A large-scale, high-quality dataset for object detection, in: Proceedings of the IEEE international conference on computer vision, pp. 8430–8439
Shao, S., Li, Z., Zhang, T., Peng, C., Yu, G., Zhang, X., Li, J., Sun, J., 2019 · 2019
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Fmssd: Feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery
Wang, P., Sun, X., Diao, W., Fu, K., 2019 · 2019
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Orsim detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features
Wu, X., Hong, D., Tian, J., Chanussot, J., Li, W., Tao, R., 2019 · 2019
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Air-sarship–1.0: High resolution sar ship detection dataset
Xian, S., Zhirui, W., Yuanrui, S., Wenhui, D., Yue, Z., Kun, F., 2019 · 2019
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Iou-adaptive deformable r-cnn: Make full use of iou for multi-class object detection in remote orientation robustsensing imagery
Yan, J., Wang, H., Yan, M., Diao, W., Sun, X., Li, H., 2019 · 2019
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Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection
Zhang, Y., Yuan, Y., Feng, Y., Lu, X., 2019 · 2019
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Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images
Fu, K., Chang, Z., Zhang, Y., Xu, G., Zhang, K., Sun, X., 2020 · 2020
Later among the works it cites.
Multi-sized object detection using spaceborne optical imagery
Haroon, M., Shahzad, M., Fraz, M.M., 2020 · 2020
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Object detection in optical remote sensing images: A survey and a new benchmark
Li, K., Wan, G., Cheng, G., Meng, L., Han, J., 2020 · 2020
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Semcity toulouse: A benchmark for building instance segmentation in satellite images
Roscher, R., Volpi, M., Mallet, C., Drees, L., Wegner, J.D., 2020 · 2020
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Sraf-net: Shape robust anchor-free network for garbage dumps in remote sensing imagery
Sun, X., Liu, Y., Yan, Z., Wang, P., Diao, W., Fu, K., 2020 · 2020
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A benchmark data set for aircraft type recognition from remote sensing images
Wu, Z.Z., Wan, S.H., Wang, X.F., Tan, M., Zou, L., Li, X.L., Chen, Y., 2020 · 2020
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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., 2020 · 2020
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Distribution shift metric learning for fine-grained ship classification in sar images
Xu, Y., Lang, H., 2020 · 2020
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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., Zhang, L., 2021 · 2021
Closest in time.
Pbnet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery
Sun, X., Wang, P., Wang, C., Liu, Y., Fu, K., 2021 · 2021
Closest in time.