[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7471/ikeee.2021.25.4.619

Vehicle Orientation Detection Using CNN

Nguyen, Huu Thang (School of Electronic and Electrical Engineering, Hongik University)
Kim, Jaemin (School of Electronic and Electrical Engineering, Hongik University)

Publication Information

Journal of IKEEE / v.25, no.4, 2021 , pp. 619-624 More about this Journal

Abstract

Vehicle orientation detection is a challenging task because the orientations of vehicles can vary in a wide range in captured images. The existing methods for oriented vehicle detection require too much computation time to be applied to a real-time system. We propose Rotate YOLO, which has a set of anchor boxes with multiple scales, ratios, and angles to predict bounding boxes. For estimating the orientation angle, we applied angle-related IoU with CIoU loss to solve the underivable problem from the calculation of SkewIoU. Evaluation results on three public datasets DLR Munich, VEDAI and UCAS-AOD demonstrate the efficiency of our approach.

Keywords

Vehicle Orientation; Vehicle Detection; Real-Time; Convolutional Neural Network(CNN);

Citations & Related Records

Reference

1	P. Dollar, R. Appel, S. Belongie, and P. Perona, "Fast feature pyramids for object detection," IEEE Trans. Pattern Anal. Mach. Intell., vol.36, no.8, pp.1532-1545, 2014. DOI
2	R. Girshick, "Fast R-CNN," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp.1440-1448, 2015.
3	Furong Shi, Tong Zhang, and Tao Zhang, "Orientation-Aware Vehicle Detection in Aerial Images via an Anchor-Free Object Detection Approach," IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 59, No.6, 2021. DOI: 10.1109/TGRS.2020.3011418 DOI
4	J. Redmon and A. Farhadi, "YOLO9000: Better, faster, stronger," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition (CVPR), pp.7263-7271, 2017.
5	Yang, Xue and Yan, Junchi and Feng, Ziming and He, Tao, "R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition (CVPR), 2019.
6	Earth Observation Center, Public Datasets: https://www.dlr.de/eoc/en/desktopdefault.aspx/ta bid-12760
7	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.1137-1149, 2017. DOI: 10.1109/TPAMI.2016.2577031 DOI
8	W. Liu et al., "SSD: Single shot multibox detector," in Proc. Eur. Conf. Comput. Vis., pp.21-37, 2016.
9	J. Ma et al., "Arbitrary-oriented scene text detection via rotation proposals," IEEE Trans. Multimedia, vol.20, no.11, pp.3111-3122, 2018. DOI
10	S. Razakarivony and F. Jurie, "Vehicle detection in aerial imagery: A small target detection benchmark," J. Vis. Commun. Image Represent., vol.34, pp. 187-203, 2016. DOI
11	Alexey Bochkovskiy, Chien-Yao Wang, and HongYuan Mark Liao, "YOLOv4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, 2020.
12	Lei Liu, Zongxu Pan, Bin Lei, "Learning a Rotation Invariant Detector with Rotatable Bounding box," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
13	Vehicle Detection in Aerial Imagery (VEDAI): https://downloads.greyc.fr/vedai/
14	Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao, "Scaled-YOLOv4: Scaling Cross Stage Partial Network," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
15	UCAS-AOD dataset: https://github.com/ming71/UCAS-AOD-benchmark
16	T. Tang, S. Zhou, Z. Deng, L. Lei, and H. Zou, "Arbitrary-oriented vehicle detection in aerial imagery with single convolutional neural networks," Remote Sens., vol.9, no.11, p.1170, 2017. DOI