한국생산제조학회 학술지 영문 홈페이지

Current Issue

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 29 , No. 4

[ Special Issue : Engineering Design of ADBL ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 29, No. 3, pp.176-181
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Jun 2020
Received 06 Apr 2020 Revised 28 Apr 2020 Accepted 06 May 2020
DOI: https://doi.org/10.7735/ksmte.2020.29.3.176

CNN을 이용한 대기차량 카운팅 알고리즘 구조에 관한 연구
정호진a ; 조민수a ; 김기범a, *

Counting Algorithm Structure for Waiting Vehicles by using CNN
Hojin Jeonga ; Minsu Joa ; Gibom Kima, *
aDepartment of Mechanical System Design Engineering, Seoul National University of Science and Technology
Correspondence to : *Tel.: +82-2-970-6342 E-mail address: gbkim@seoultech.ac.kr (Gibom Kim).

Funding Information ▼

Abstract

Increased computing power and advanced deep learning technology have enabled computers to effectively deal with problems that cannot be solved by ordinary people. Many attempts have been made to utilize deep learning technology to analyze road images and efficiently control crossroad vehicle flow. In this research, a new methodology is proposed for identifying the number of vehicles on the road using CNN (convolution neural network), deep learning technology that specializes in image classification. Unlike previous studies that used regression methods and video frames as input, this study determined the number of vehicles using real-time photographic images and classification methods for one lane. An experiment was conducted to find the optimal combination of variables using sensitivity analysis. The optimal network determined the number of vehicles on one lane with a high accuracy of 98.31%.


Keywords: Convolutional neural network, Vehicle counting, Sensitivity analysis, Classification

Acknowledgments

본 연구는 서울과학기술대학교 교내 일반과제 연구비 지원으로 수행되었습니다.


References
1. Sindagia, V., Patel, V., 2017, A Survey of Recent Advance in CNN-based Single Image Crowd Counting and Density Estimation, Pattern Recognition Letters., 107 3-16.
2. Fu, M., Xu, P., Li, X., Liu, Q., Ye, M., Zhu, C., 2015, Fast Crowd Density Estimation With Convolutional Neural Networks, Engineering Applications of Artificial Intelligence, 43 81-88.
3. Zhang, C., Li, H., Wang, X., Yang, X., 2015, Deep People Counting in Extremely Dense Crowds, Proc. ACM international Conference on Multimedia. Autumn conf., 1299.
4. Zhang, C., Li, H., Wang, X., Yang, X., 2015, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, IEEE Conference on Computer Vision and pattern Recognition, 1 833.
5. Walach, E., Wolf, L., 2016, Learning to Count With cnn Boosting, Proc. Computer Vision-ECCV. Autumn conf., 660.
6. Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y., 2016, Single Image Crowd Counting via Multi-column Convolutional Neural Network, IEEE Conference on Computer Vision and Pattern Recognition, 589.
7. Onoro-Rubio, D., L´opez-Sastre, R. J., 2016, Towards Perspective-free Object Counting With Deep Learning, Proc. Computer Vision – ECCV, 615.
8. Zhao, Z., Li, H., Zhao, R., Wang, X., 2016, Crossing-line Crowd Counting With Two-phase Deep Neural Networks, Proc. Computer Vision – ECCV 2016, 712.
9. Sun, M., Wang, Y., Li, T., Lv, J., Wu, J., 2017, Vehicle Counting in Crowded Scenes With Muliti-channel and Multi-task Convolutional Neural Networks, Journal of Visual Communication and Image Representation, 49 412-419.
10. Chung, J., Sohn, K., 2018, Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems, 19:5 1670-1675.
11. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., 1989, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation winter 1989, 1:4 541-551.
12. Krizhevsky, A., Sutskever, I., Hinton, G., 2017, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, 60:6 84-90.

Hojin Jeong

B.Sc. candidate in the Department of Mechanical System Design Engineering, Seoul National University of Science and Technology.His research interest is Computer Vision.His research interest is Artificial Intelligence.

E-mail: hojin.jeong@gmail.com

Minsu Jo

B.Sc. candidate in the Department of Mechanical System Design Engineering, Seoul National University of Science and Technology.His research interest is Computer Vision.His research interest is Artificial Intelligence.

E-mail: whalstn098@naver.com

Gibom Kim

Professor in the Department of Mechanical System Design Engineering, Seoul National University of Science and Technology.His research interest is computer vision.

E-mail: gbkim@seoultech.ac.kr