[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2021.26.01.045

Ensemble Deep Network for Dense Vehicle Detection in Large Image

Yu, Jae-Hyoung (School of Electronic Engineering, Soongsil University)
Han, Youngjoon (Dept. Smart Systems Software, Soongsil University)
Kim, JongKuk (Global future Education Institute, Soongsil University)
Hahn, Hernsoo (School of Electronic Engineering, Soongsil University)

Publication Information

Journal of the Korea Society of Computer and Information / v.26, no.1, 2021 , pp. 45-55 More about this Journal

Abstract

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

Keywords

Ensemble Deep-Learning Network; Voting Map; Dense Small Object Detection; High Resolution Image; Dynamic Windows;

Citations & Related Records

Reference

1	Q. QIN, Y. YUAN, R. LU, "A New Approach to Object Recognition on High Resolution Satellite Image," International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000.
2	M. Muller and K. Segl, "Object Recognition Based on High Spatial Resolution Panchromatic Satellite Imagery," Proc. ISPRS Joint Workshop - Sensors and Mappingfrom Space 1999 (Hannover).
3	M. Yang, N. Otterness, T. Amert, J. Bakita, J. H. Anderson and F. D. Smith, "Avoiding Pitfalls when Using NVIDIA GPUs for Real-Time Tasks in Autonomous Systems," ECRTS, Euromicro Conference on Real-Time Systems. No. 20, pp. 20:1-20:21, 2018.
4	M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth and B. Schiele, "The cityscapes dataset for semantic urban scene understanding." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213-3223, 2016.
5	O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "ImageNet large scale visual recognition challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Apr. 2015. DOI
6	Y. Lv et al., "Traffic Flow Prediction with Big Data: A Deep Learning Approach," IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 2, 2015, pp. 865-73. DOI
7	L. B. Guaman, J. E. Naranjo and A. Ortiz, "Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU," Electronics 2020, 9, 589. DOI
8	A. Marcu and M. Leordeanu, "Dual local-global contextual pathways for recognition in aerial imagery," Computing Research Repository (CRR) abs/1605.05462.
9	S. Razakarivony and F. Jurie, "Vehicle detection in aerial imagery: A small target detection benchmark," Journal of Visual Communication and Image Representation, 34, 187-203, 2016. DOI
10	H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar and P. A. Muller, "Deep Neural Network Ensembles for Time Series Classification," International Joint Conference on Neural Networks (IJCNN), arXiv:1605.05462, 2019.
11	H. Noh, S. Hong and B. Han, "Learning Deconvolution Network for Semantic Segmentation," Computer Vision and Pattern Recognition, arXiv:1505.04366, 2015.
12	S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Computer Vision and Pattern Recognition Cite as: arXiv:1506.01497
13	W. Liu et al., "SSD: Single shot multibox detector," In Proc. ECCV, pp. 21-37, 2016.
14	H. Tayara and K. T. Chong, "Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network," MDPI, Sensors 2018, 18(10), 3341. 2018. DOI
15	J. Redmon and A. Farhadi, "YOLO9000 : Better, Faster, Stronger", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.779-788, 2016.
16	M. Gao, R. Yu, A. Li, V. I. Morariu and L. S. Davis, "Dynamic Zoom-in Network for Fast Object Detection in Large Images," Computer Vision and Pattern Recognition (CVPR), arXiv:1711.05187, 2018.
17	V. Ruzicka and F. Franchetti, "Fast and accurate object detection in high resolution 4K and 8K video using GPUs," IEEE High Performance extreme Computing Conference (HPEC), arXiv:1810.10551, 2018.
18	Z. Wu, X. Chen, Y. Gao and Y. Li, "Rapid Target Detection in High Resolution Remote Sensing Images Using YOLO Model," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3, 2018.
19	Y. Ren, C. Zhu and S. Xiao, "Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN," MDPI, SCI 2018, 8(5), 813, 2018.
20	D. M. W. POWERS, "EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION," Journal of Machine Learning Technologies, Volume 2, Issue 1, pp.37-63, 2011.