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NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.4, 2021년, pp.747 - 761
Omar, Wael (Department of Geoinformatics, University of Seoul) , Oh, Youngon (Department of Geoinformatics, University of Seoul) , Chung, Jinwoo (Department of Geoinformatics, University of Seoul) , Lee, Impyeong (Department of Geoinformatics, University of Seoul)
With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algo...
Ajay, A., V. Sowmya and K.P. Soman, 2017. Vehicle detection in aerial imagery using eigen features, Proc. of 2017 International Conference on Communication and Signal Processing ICCSP, Chennai, IN, Apr. 6-8, pp. 1620-1624.
Ammour, N., H. Alhichri, Y. Bazi, B. Benjdira, N. Alajlan, and M. Zuair, 2017. Deep Learning Approach for Car Detection in UAV Imagery, Remote Sens, 9(4): 312.
Azevedo, C.L., J.L. Cardoso, M. Ben-Akiva, J.P. Costeira, and M. Marques, 2014. Automatic VehicleTrajectory Extraction by Aerial Remote Sensing, Procedia - Social and Behavioral Sciences, 111: 849-858.
Bochkovskiy, A., C.Y.Wang, and H.Y.M Liao, 2020. YOLOv4: Optimalspeed and accuracy of object detection, arXiv print, arXiv: 2004.10934v1.
Chen, X., S. Xiang, C.-L. Liu, and C.-H. Pan, 2014. Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, 11(10): 1797-1801.
Cheng, P. Z., 2009, Detecting and Counting Vehicles from SMALL LOW-COST UAV IMAGES, Proc. of ASPRS 2009 Annual Conference, Baltimore, MD, Mar. 9-13, pp. 1-7.
Deng,J.,W. Dong,R. Socher, L.J. Li, K. Li, and L. Fei-Fei, 2009. ImageNet:Alarge-scale hierarchical image database, Proc of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, US, Jun. 20-25, pp. 248-255.
Everingham, M., S.M.A. Eslami, L.V. Gool, C.K.I. Williams, J. Winn, and A. Zisserman, 2014. The Pascal visual object Classes challenge: A retrospective, International Journal of Computer Vision, 111: 98-136.
Everingham, M.L.V. 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.
Kim, C.E., M.M.D. Oghaz, J. Fajtl, V. Argyriou, and P. Remagnino, 2018. A comparison of embedded deep learning methods for person detection, arXiv PrePrint, arXiv:1812.03451.
Lewandowski, M., B. Placzek, M. Bernas, and P. Szymala, 2018. Road traffic monitoring system based on mobile devices and bluetooth low energy beacons, Wireless Communications and Mobile Computing, 2018: 1-12.
Lin H.-Y., K.-C. Tu, and C.-Y. Li, 2020. VAID: An Aerial Image Dataset for Vehicle Detection and Classification, IEEE Access, 8: 212209-212219.
Lin, T.-Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar C, and L. Zitnick, 2014. Microsoft COCO: Common objectsin context, ECCV.
Liu, K. and G. Mattyus, 2015. Fast multiclass vehicle detection on aerial images, IEEE Geoscience and Remote Sensing Letters, 12(9): 1938-1942.
Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, and A.C. Berg, 2016. SSD: Single shot multibox detector, Proc. of European Conference on Computer Vision 2016, Cham, CH, Oct. 8-16, pp. 21-37.
Lu, J., C. Ma, L. Li, X. Xing, Y. Zhang, Z. Wang, and J. Xu, 2018. A Vehicle Detection Method for Aerial Image ased on YOLO. Journal of Computer and Communications, 6(11): 98-107.
Qiu, Y., (2014). Video-Based Vehicle Detection in Intelligent Transportation System, Master Thesis, Jilin University, Chang Chun, CN.
Razakarivony, S. and F. Jurie, 2016. Vehicle detection in aerial imagery: A small target detection benchmark, Journal of Visual Communication and Image Representation, 34: 187-203.
Redmon, J. and A. Farhadi, 2017. YOLO9000: Better, Faster, Stronger. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, US, Jul. 21-26, Vol. 1, pp. 6517-6525.
Redmon, J. and A. Farhadi, 2018. YOLO v3: An Incremental Improvement, arXiv preprint, arxiv: 1804.02767.
Redmon, J., S. Divvala, R. Girshick, and A. Farhadi, 2016. You Only Look Once:Unified, Real-Time Object Detection, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, LAS VEGAS, NV, US, Jun. 27-30, Vol.1, pp. 779-788.
Simonyan, K. and A. Zisserman, 2015. A Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint, arXiv: 1409.1556.
Sivaraman, S. and M.M. Trivedi, 2010. A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking, IEEE Transactions on Intelligent Transportation Systems, 11(2): 267-276.
Mundhenk, T.N., G. Konjevod, W.A. Sakla, and K. Boakye, 2016. A large contextual dataset for classification, detection and counting of cars with deep learning, Proc. of European Conference on Computer Vision 2014, Zurich, CH, Sep. 6-12, Vol. 4, pp. 740-755.
Tehrani Niknejad, H., A. Takeuchi, S. Mita, and D. McAllester, 2012. On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter with Improved Likelihood, IEEE Transactions on Intelligent Transportation Systems, 13(2): 748-758.
Xi, X., Z. Yu, Z. Zhan, and C. Tian, 2019. Multi-task Cost-sensitive-Convolutional Neural Network for Car Detection, IEEE Access, 7: 98061-98068.
Xia, G.-S. X. Bai,J. Ding, Z. Zhu, S. Belongie,J. Luo, M. Datcu, M. Pelillo, and L. Zhang, 2018. Dota: A large-scale dataset for object detection in aerial images, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, US,Jun. 19-21, pp. 3974-3983.
Yu, H., G. Li,W. Zhang, Q. Huang, D. Du, Q.Tian, and N . Sebe, 2019. The unmanned aerial vehicle Benchmark: Object Detection, tracking and baseline, International Journal of Computer Vision, 128(5): 1141-1159.
Zhu, H., X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, 2015. Orientation robust object detection in aerial images using deep convolutional neural network, Proc. of 2015 IEEE International Conference on Image Processing(ICIP), Quebec, QC, CA, Sep. 27-30, pp. 3735-3739.
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