Ensemble Deep Learning Using Faster R-CNN and Genetic Algorithm for Vehicle Detection in UAV Images

References

• J. Leitloff, D. Rosenbaum, F. Kurz, O. Meynberg, and P. Reinartz, “An operational system for estimating road traffic information from aerial images,” Remote. Sens., Vol. 6, no. 11, pp. 11315–41, 2014. doi:10.3390/rs61111315
   Google Scholar
• S. Srivastava, S. Narayan, and S. Mittal, “A survey of deep learning techniques for vehicle detection from UAV images,” J. Syst. Archit., Vol. 117, pp. 102152, 2021. doi:10.1016/j.sysarc.2021.102152
   Web of Science ®Google Scholar
• Y. Xu, G. Yu, Y. Wang, X. Wu, and Y. Ma, “Car detection from low-altitude UAV imagery with the faster R-CNN,” J. Adv. Transp., Vol. 2017, no. 2823617, pp. 1–10, 2017.
   Google Scholar
• T. Zhao, and R. Nevatia, “Car detection in low resolution aerial image,” in 8th IEEE International Conference on Computer Vision, Washington, 2001.
   Google Scholar
• T. Moranduzzo, and F. Melgani, “Automatic car counting method for unmanned aerial vehicle images,” IEEE Trans. Geosci. Remote Sens., Vol. 52, pp. 1635–47, 2014. doi:10.1109/TGRS.2013.2253108
   Web of Science ®Google Scholar
• G. L. Huang, J. He, Z. Xu, and G. Huang, “A combination model based on transfer learning for waste classification,” Concurr. Comput. Pract. Exp., Vol. 32, no. 19, 2020. doi:10.1002/cpe.5751
   Web of Science ®Google Scholar
• X. Chen, S. Xiang, C.-L. Liu, and C.-H. Pan, “Vehicle detection in satellite images by hybrid deep convolutional neural networks,” IEEE Geosci. Remote Sens. Lett., Vol. 11, pp. 1797–801, 2014. doi:10.1109/LGRS.2014.2309695
   Web of Science ®Google Scholar
• K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 37, no. 9, pp. 1904–16, 2015. doi:10.1109/TPAMI.2015.2389824
   PubMed Web of Science ®Google Scholar
• R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–7.
   Google Scholar
• R. Girshick, “Fast R-CNN,” in Proceedings of the 15th IEEE International Conference on Computer Vision, 2015, pp. 1440–8.
   Google Scholar
• S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 39, no. 6, pp. 1137–49, 2017. doi:10.1109/TPAMI.2016.2577031
   PubMed Web of Science ®Google Scholar
• P. Viola, and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. 511–8.
   Google Scholar
• P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 32, no. 9, pp. 1627–45, 2010. doi:10.1109/TPAMI.2009.167
   PubMed Web of Science ®Google Scholar
• Y. Xu, G. Yu, Y. Wang, X. Wu, and Y. Ma, “A hybrid vehicle detection method based on Viola-Jones and HOG + SVM from UAV images,” Sens., Vol. 16, no. 8, pp. 1325, 2016. doi:10.3390/s16081325
   PubMed Web of Science ®Google Scholar
• X. Luo, et al. “Fast automatic vehicle detection in UAV images using convolutional neural networks,” Remote. Sens., Vol. 12, no. 12, pp. 1994, 2020.
   Google Scholar
• N. Ammour, H. Alhichri, Y. Bazi, B. Benjdira, N. Alajlan, and M. Zuair, “Deep learning approach for car detection in UAV imagery,” Remote. Sens., Vol. 9, no. 4, pp. 1–15, 2017.
   Google Scholar
• Y. Bazi, and F. Melgani, “Convolutional SVM networks for object detection in UAV imagery,” IEEE Trans. Geosci. Remote Sens., Vol. 56, pp. 3107–18, 2018. doi:10.1109/TGRS.2018.2790926
   Web of Science ®Google Scholar
• Y. Li, F. Melgani, and B. He, “Fully convolutional SVM for car detection in UAV imagery,” in Geoscience and Remote Sensing Symposium, 2019, pp. 2451–4.
   Google Scholar
• A. Solanki, R. Bamrara, K. Kumar, and N. Singh, “VEDL: A novel video event searching technique using deep learning,” Perspect., Vol. 2, no. 4, pp. 6–8, 2020.
   Google Scholar
• K. Kumar, “Text query based summarized event searching interface system using deep learning over cloud,” Multimed. Tools. Appl., Vol. 80, no. 7, pp. 11079–94, 2021. doi:10.1007/s11042-020-10157-4
   Web of Science ®Google Scholar
• S. Sharma, K. Kumar, and N. Singh, “Deep Eigen space based ASL recognition system,” IETE. J. Res., 1–11, 2020. Published online on July 13, 2020.
   Web of Science ®Google Scholar
• K. Kumar, S. Sinha, and P. Manupriya, “D-PNR: deep license plate number recognition,” in Proceedings of 2nd International Conference on Computer Vision & Image Processing, Springer, Singapore, 2018, pp. 37–46.
   Google Scholar
• K. Kumar, and D. D. Shrimankar, “F-DES: Fast and deep event summarization,” IEEE Trans. Multimed., Vol. 20, no. 2, pp. 323–34, 2017. doi:10.1109/TMM.2017.2741423
   Web of Science ®Google Scholar
• K. Kumar, and D. D. Shrimankar, “Deep event learning boost-up approach: DELTA,” Multimed. Tools. Appl., Vol. 77, no. 20, pp. 26635–55, 2018. doi:10.1007/s11042-018-5882-z
   Web of Science ®Google Scholar
• K. Kumar, D. D. Shrimankar, and N. Singh, “Event bagging: A novel event summarization approach in multiview surveillance videos,” in 2017 International Conference on Innovations in Electronics, Signal Processing and Communication, 2017, pp. 106–11.
   Google Scholar
• M. Shokouhifar, and A. Jalali, “A new evolutionary based application specific routing protocol for clustered wireless sensor networks,” AEU - Int. J. Electron. Commun., Vol. 69, no. 1, pp. 432–41, 2015. doi:10.1016/j.aeue.2014.10.023
   Web of Science ®Google Scholar
• I. Bozcan, and E. Kayacan, “AU-AIR: A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance,” in International Conference on Robotics and Automation, 2020, pp. 8504–10.
   Google Scholar
• M. Shokouhifar, and A. Jalali, “Optimized Sugeno fuzzy clustering algorithm for wireless sensor networks,” Eng. Appl. Artif. Intell., Vol. 60, pp. 16–25, 2017. doi:10.1016/j.engappai.2017.01.007
   Web of Science ®Google Scholar
• M. Shokouhifar, M. M. Sabbaghi, and N. Pilevari, “Inventory management in blood supply chain considering fuzzy supply/demand uncertainties and lateral transshipment,” Transfus. Apher. Sci., Vol. 60, no. 3, pp. 103103, 2021. doi:10.1016/j.transci.2021.103103
   PubMed Web of Science ®Google Scholar
• M. Shokouhifar, “FH-ACO: Fuzzy heuristic-based ant colony optimization for joint virtual network function placement and routing,” Appl. Soft. Comput., Vol. 107, pp. 107401, 2021. doi:10.1016/j.asoc.2021.107401
   Web of Science ®Google Scholar