Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.4.6

Aerial Dataset Integration For Vehicle Detection Based on YOLOv4  

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)
Publication Information
Korean Journal of Remote Sensing / v.37, no.4, 2021 , pp. 747-761 More about this Journal
Abstract
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 algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.
Keywords
Vehicle Detection; Aerial Image; YOLO; VAID; UAVD; DOTA;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.
2 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.   DOI
3 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.
4 Liu, K. and G. Mattyus, 2015. Fast multiclass vehicle detection on aerial images, IEEE Geoscience and Remote Sensing Letters, 12(9): 1938-1942.   DOI
5 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.
6 Qiu, Y., (2014). Video-Based Vehicle Detection in Intelligent Transportation System, Master Thesis, Jilin University, Chang Chun, CN.
7 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.   DOI
8 Redmon, J. and A. Farhadi, 2018. YOLO v3: An Incremental Improvement, arXiv preprint, arxiv: 1804.02767.
9 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.
10 Simonyan, K. and A. Zisserman, 2015. A Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint, arXiv: 1409.1556.
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 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.
19 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.   DOI
20 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.
21 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.   DOI
22 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.   DOI
23 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.   DOI
24 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.   DOI
25 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.
26 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.   DOI
27 Bochkovskiy, A., C.Y.Wang, and H.Y.M Liao, 2020. YOLOv4: Optimalspeed and accuracy of object detection, arXiv print, arXiv: 2004.10934v1.
28 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.   DOI
29 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.