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Research Article

Vector encoded bounding box regression for detecting remote-sensing objects with anchor-free methods

ORCID Icon, ORCID Icon, & ORCID Icon
Pages 693-713 | Received 23 Apr 2020, Accepted 29 Jul 2020, Published online: 19 Nov 2020

References

  • Cai, Z., and V. Nuno. 2018. “Cascade R-CNN: Delving into High Quality Object Detection”. IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, June 18–22, 6154–6162. doi:10.1109/cvpr.2018.00644
  • Cheng, G., and J. Han. 2016. “A Survey on Object Detection in Optical Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 117: 11–28. doi:10.1016/j.isprsjprs.2016.03.014.
  • Cheng, G., J. Han, P. Zhou, and D. Xu. 2018. “Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection.” IEEE Transactions on Image Processing 28 (1): 265–278. doi:10.1109/tip.2018.2867198.
  • Cheng, G., J. Han, P. Zhou, and L. Guo. 2014. “Multi-class Geospatial Object Detection and Geographic Image Classification Based on Collection of Part Detectors.” ISPRS Journal of Photogrammetry and Remote Sensing 98: 119–132. doi:10.1016/j.isprsjprs.2014.10.002.
  • Cheng, G., P. Zhou, and J. Han. 2016. “Learning Rotation-invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 54 (12): 7405–7415. doi:10.1109/tgrs.2016.2601622.
  • Cheng, G., Y. Si, H. Hong, X. Yao, and L. Guo. 2020. “Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 1–5. doi:10.1109/lgrs.2020.2975541.
  • Dai, J., Y. Li, K. He, and J. Sun. 2016. “R-FCN: Object Detection via Region-based Fully Convolutional Networks.” Neural Information Processing Systems (NIPS), Barcelona, Spain, December 5–10, 379–387
  • Dalal, N., and B. Triggs. 2005. “Histograms of Oriented Gradients for Human Detection” IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 20–26, 886–893. doi:10.1109/cvpr.2005.177
  • Deng, J., W. Dong, R. Socher, L. Li, and L. Fei-Fei. 2009. “ImageNet: A Large-Scale Hierarchical Image Database.” IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, 20–25. doi:10.1109/cvpr.2009.5206848
  • Duan, K., S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian. 2019. “Centernet: Keypoint Triplets for Object Detection”. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, October 27 - November 2, 6569–6578. doi:10.1109/iccv.2019.00667
  • Everingham, M., L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2010. “The PASCAL Visual Object Classes (VOC) Challenge.” International Journal of Computer Vision 88 (2): 303–338. doi:10.1007/s11263-009-0275-4.
  • Felzenszwalb, P. F., R. Girshick, D. Mcallester, and D. Ramanan. 2010. “Object Detection with Discriminatively Trained Part Based Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (9): 1627–1645. doi:10.1109/tpami.2009.167.
  • Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, June 23–28, 580–587. doi:10.1109/cvpr.2014.81
  • Girshick, R. 2015. “Fast R-CNN”. International Conference on Computer Vision, Santiago, Chile, December 13–16, 1440–1448. doi:10.1109/iccv.2015.169
  • Guo, W., W. Yang, H. Zhang, and G. Hua. 2018. “Geospatial Object Detection in High Resolution Satellite Images Based on Multi-scale Convolutional Neural Network.” Remote Sensing 10 (1): 131. doi:10.3390/rs10010131.
  • Han, J., D. Zhang, and G. Cheng. 2015. “Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-level Feature Learning.” IEEE Transactions on Geoscience and Remote Sensing 53: 3325–3337. doi:10.1109/tgrs.2014.2374218.
  • Han, X., Y. Zhong, and L. Zhang. 2017. “An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery.” Remote Sensing 9: 666. doi:10.3390/rs9070666.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition”. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 27–30, 770–778. doi:10.1109/cvpr.2016.90
  • He, K., G. Gkioxari, P. Dollar, and R. Girshick. 2017. “Mask R-CNN”. IEEE/CVF International Conference on Computer Vision (ICCV), Venice, Italy, October 22–29, 2961–2969. doi:10.1109/iccv.2017.322
  • Huang, L., Y. Yang, Y. Deng, and Y. Yu. 2015. “DenseBox: Unifying Landmark Localization with End to End Object Detection”. arXiv preprint arXiv:1509.04874.
  • Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ” arXiv preprint arXiv:1502.03167.
  • Kilic, E., and S. Ozturk. 2019. “A Subclass Supported Convolutional Neural Network for Object Detection and Localization in Remote-sensing Images.” International Journal of Remote Sensing 40 (11): 7193. doi:10.1080/01431161.2018.1562260.
  • Law, H., and J. Deng. 2019. “CornerNet: Detecting Objects as Paired Keypoints.” International Journal of Computer Vision 128 (3): 642–656. doi:10.1007/s11263-019-01204-1.
  • Li, K., G. Cheng, S. Bu, and X. You. 2017. “Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 56 (4): 2337–2348. doi:10.1109/tgrs.2017.2778300.
  • Li, K., G. Wan, G. Cheng, L. Meng, and J. Han. 2020. “Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark.” ISPRS Journal of Photogrammetry and Remote Sensing 159: 296–307. doi:10.1016/j.isprsjprs.2019.11.023.
  • Lin, T. Y., M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. “Microsoft Coco: Common Objects in Context”. Proc. European Conference on Computer Vision, Zurich, Switzerland, September 6–12, 740–755. doi:10.1007/978-3-319-10602-1_48
  • Lin, T. Y., P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie. 2016. “Feature Pyramid Networks for Object Detection”. Proc. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July 21–26, 2117–2125. doi:10.1109/cvpr.2017.106
  • Lin, T. Y., P. Goyal, R. Girshick, K. He, and P. Dollar. 2017c. “Focal Loss for Dense Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2): 318–327. doi:10.1109/tpami.2018.2858826.
  • Liu, L., and Z. Shi. 2014. “”Airplane Detection Based On Rotation Invariant And Sparse Coding In Remote Sensing Images. ”.” Optik 125 (18): 5327–5333. doi:10.1016/j.ijleo.2014.06.062.
  • Liu, S., L. Qi, H. Qin, J. Shi, and J. Jia. 2018. “Path Aggregation Network for Instance Segmentation”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, June 18–22, 8759–8768. doi:10.1109/cvpr.2018.00913
  • Liu, W., D. Anguelov, D. Erhan, C. Szegedy, and S. Reed. 2016. “SSD: Single Shot MultiBox Detector”. Proc. European Conference on Computer Vision, Amsterdam, The Netherlands, October 11–14, 21–37. doi:10.1007/978-3-319-46448-0_2
  • Liu, X., and X. Di. 2020. “TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks”. arXiv preprint arXiv:2003.09855.
  • Nair, V., and G. E. Hinton. 2010. “Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair”. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, June 21–24, 807–814. doi:10.5555/3104322.3104425
  • Pan, X., and J. Zhao. 2019. “A Central-point-enhanced Convolutional Neural Network for High-resolution Remote-sensing Image Classification.” International Journal of Remote Sensing 38 (23): 6554–6581. doi:10.1080/01431161.2017.1362131.
  • Qian, X., S. Lin, G. Cheng, X. Yao, H. Ren, and W. Wang. 2020. “Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion.” Remote Sensing 12 (1): 143. doi:10.3390/rs12010143.
  • Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. “You Only Look Once: Unified Real-time Object Detection”. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 27–30, 779–788. doi:10.1109/cvpr.2016.91
  • Ren, S., K. He, R. Girshick, and J. Sun. 2017. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6): 1137–1149. doi:10.1109/tpami.2016.2577031.
  • Rezatofighi, H., N. Tsoi, J. Y. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. “Generalized Intersection over Union: A Metric and a Loss for Bounding Box Regression”. IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16–20, 658–666. doi:10.1109/cvpr.2019.00075
  • Tian, Z., C. Shen, H. Chen, and T. He. 2019. “FCOS: Fully Convolutional One-Stage Object Detection”. IEEE International Conference on Computer Vision, Seoul, South Korea, October 27 - November 2, 9626–9635. doi:10.1109/iccv.2019.00972
  • Viola, P., and J. Michael. 2001. “Robust Real-Time Face Detection”. IEEE International Conference on Computer Vision, Vancouver, Canada, July 7–14, 747. doi:10.1109/iccv.2001.937709
  • Wu, Y., and K. He. 2018. “Group Normalization.” International Journal of Computer Vision 128 (3): 742–755. doi:10.1007/s11263-019-01198-w.
  • Yang, X., H. Sun, K. Fu, J. Yang, X. Sun, M. Yan, and Z. Guo. 2018. “Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks.” Remote Sensing 10 (1): 132. doi:10.3390/rs10010132.
  • Yang, Z., S. Liu, H. Hu, L. Wang, and S. Lin. 2019. “RepPoints: Point Set Representation for Object Detection”. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, October 27 - November 2, 9657–9666. doi:10.1109/iccv.2019.00975
  • Yu, J., Y. Jiang, Z. Wang, Z. Cao, and T. Huang. 2016. “UnitBox: An Advanced Object Detection Network”. Proceedings of the 2016 ACM on Multimedia Conference - MM ’16, Amsterdam, The Netherlands, October 15–19, 516–520. doi:10.1145/2964284.2967274
  • Zhang, F., B. Du, L. Zhang, and M. Xu. 2016. “Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection.” IEEE Transactions on Geoscience and Remote Sensing 54 (9): 5553–5563. doi:10.1109/tgrs.2016.2569141.
  • Zhang, L., Z. Shi, and J. Wu. 2015. “A Hierarchical Oil Tank Detector with Deep Surrounding Features for High-Resolution Optical Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (10): 4895–4909. doi:10.1109/jstars.2015.2467377.
  • Zhang, W., X. Sun, K. Fu, C. Wang, and H. Wang. 2014. “Object Detection in High-resolution Remote Sensing Images Using Rotation Invariant Parts Based Model.” IEEE Geoscience & Remote Sensing Letters 11 (1): 74–78. doi:10.1109/lgrs.2013.2246538.
  • Zheng, Z., P. Wang, W. Liu, J. Li, R. Ye, and D. Ren. 2019. “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression.” The AAAI Conference on Artificial Intelligence (AAAI) 34 (7): 12993–13000. doi:10.1609/aaai.v34i07.6999.
  • Zhong, P., and R. Wang. 2007. “A Multiple Conditional Random Field’s Ensemble Framework for Urban Area Detection in Remote Sensing Optical Images.” IEEE Transactions on Geoscience and Remote Sensing 45 (12): 3978–3988. doi:10.1109/tgrs.2007.907109.
  • Zhong, Y., X. Han, and L. Zhang. 2018. “Multi-class Geospatial Object Detection Based on a Position-sensitive Balancing Framework for High Spatial Resolution Remote Sensing Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 138: 281–294. doi:10.1016/j.isprsjprs.2018.02.014.
  • Zhou, X., J. Zhuo, and P. Krhenbühl. 2019. “Bottom-up Object Detection by Grouping Extreme and Center Points”. IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16–20, 850–859. doi:10.1109/cvpr.2019.00094
  • Zhu, C., Y. He, and M. Savvides. 2019. “Feature Selective Anchor-free Module for Single-shot Object Detection”. IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16–20, 840–849. doi:10.1109/cvpr.2019.00093

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