References
- Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, et al. 2016. Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems. Vol. abs/1603.04467. ArXiv.
- Cai, Z., Q. Fan, R. S. Feris, and N. Vasconcelos (2016). “A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection”. In ECCV, Amsterdam, The Netherlands.
- Dai, J., Y. Li, K. He, and J. Sun. 2016. “R-fcn: Object Detection via Region-based Fully Convolutional Networks.” ArXiv abs/1605.06409: n.pag.
- Deng, Z., H. Sun, S. Zhou, and J. Zhao. 2019. “Learning Deep Ship Detector in Sar Images from Scratch.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 4021–4039. doi:https://doi.org/10.1109/TGRS.2018.2889353.
- El-Darymli, K., E. W. Gill, P. Mcguire, D. Power, and C. Moloney. 2016. “Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-art Review.” IEEE Access 4: 6014–6058. doi:https://doi.org/10.1109/ACCESS.2016.2611492.
- Eldhuset, K. 1996. “An Automatic Ship and Ship Wake Detection System for Spaceborne Sar Images in Coastal Regions.” IEEE Transactions on Geoscience and Remote Sensing 34 (4): 1010–1019. doi:https://doi.org/10.1109/36.508418.
- Everingham, M., S. M. A. Eslami, L. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman. 2015. “The Pascal Visual Object Classes Challenge: A Retrospective.” International Journal of Computer Vision 111 (1): 98–136. doi:https://doi.org/10.1007/s11263-014-0733-5.
- Fu, C.-Y., W. Liu, A. Ranga, A. Tyagi, and A. C. Berg. 2017. “Dssd: Deconvolutional Single Shot Detector.” ArXiv abs/1701.06659:n.pag.
- Girshick, R. B., J. Donahue, T. Darrell, and J. Malik (2014). “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”. 2014 IEEE Conference on Computer Vision and Pattern Recognition,580–587. Columbus, Ohio.
- Girshick, R. B. (2015). “Fast R-cnn”. 2015 IEEE International Conference on Computer Vision (ICCV), 1440–1448. Santiago, Chile.
- Gui, Y., X. Li, and L. Xue. 2019. “A Multilayer Fusion Light-head Detector for Sar Ship Detection.” Sensors (Basel, Switzerland) 19 (5): 1124. doi:https://doi.org/10.3390/s19051124.
- He, K., X. Zhang, S. Ren, and J. Sun (2016). “Deep Residual Learning for Image Recognition”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. Las Vegas, Nevada.
- He, K., G. Gkioxari, P. Dollár, and R. B. Girshick (2017). “Mask R-cnn”. 2017 IEEE International Conference on Computer Vision (ICCV),2980–2988. Venice, Italy.
- Jiang, S., C. Wang, B. Zhang, and H. Zhang (2012). “Ship Detection Based on Feature Confidence for High Resolution Sar Images”. 2012 IEEE International Geoscience and Remote Sensing Symposium, 6844–6847. California, USA.
- Krizhevsky, A., Ilya Sutskever and Geoffrey E. Hinton. 2012. “ImageNet classification with deep convolutional neural networks.” Communications of the ACM 60: 84–90.
- LeCun, Y., B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel. 1989. “Backpropagation Applied to Handwritten Zip Code Recognition.” Neural Computation 1 (4): 541–551. doi:https://doi.org/10.1162/neco.1989.1.4.541.
- Li, J., C. Qu, and J. Shao. 2017. “Ship Detection in Sar Images Based on an Improved Faster R-cnn.” In 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), 1–6. Beijing, China.
- Li, T., Z. Liu, R. Xie, and L. Ran. 2018a. “An Improved Superpixel-level Cfar Detection Method for Ship Targets in High-resolution Sar Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (1): 184–194. doi:https://doi.org/10.1109/JSTARS.2017.2764506.
- Li, Z., C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun. 2018b. Detnet: A Backbone Network for Object Detection. Vol. abs/1804.06215. ArXiv.
- Lin, T.-Y., P. Goyal, R. B. Girshick, K. He, and P. Dollár. 2020. “Focal Loss for Dense Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2): 318–327. doi:https://doi.org/10.1109/TPAMI.2018.2858826.
- Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C.-Y. Fu, and A. C. Berg (2016). “Ssd: Single Shot Multibox Detector”. In ECCV, Amsterdam, The Netherlands.
- Redmon, J., S. K. Divvala, R. B. Girshick, and A. Farhadi (2016). “You Only Look Once: Unified, Real-time Object Detection”. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. United States La Vegas, USA.
- Redmon, J., and A. Farhadi (2017). “Yolo9000: Better, Faster, Stronger”. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517–6525. Hawaii Convention Center, Hawaii, USA.
- Redmon, J., and A. Farhadi. 2018. Yolov3: An Incremental Improvement. Vol. abs/1804.02767. ArXiv.
- Ren, S., K. He, R. B. 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:https://doi.org/10.1109/TPAMI.2016.2577031.
- Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, et al. 2015. “Imagenet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–252. doi:https://doi.org/10.1007/s11263-015-0816-y.
- Shen, Z., H. Shi, J. Yu, H. Phan, R. S. Feris, L. Cao, D. Liu, X. Wang, T. T. Huang, and M. Savvides. 2019. Improving Object Detection from Scratch via Gated Feature Reuse. BMVC, Cardiff University.
- Simonyan, K., and A. Zisserman. 2015. “Very Deep Convolutional Networks for Large-scale Image Recognition.” CoRR abs/1409.1556: n.pag.
- Wu, X., D. Sahoo, and S. C. Hoi. 2020. “Recent Advances in Deep Learning for Object Detection.” Neurocomputing. n.pag.
- Zhu, R., S. Zhang, X. Wang, L. Wen, H. Shi, L. Bo, and T. Mei (2019). “Scratchdet: Training Single-shot Object Detectors from Scratch”. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), . 2263–2272. Long Beach, USA.