Implementation Counting and Yolo Object Detection Methods for Identification Degree of Road Saturation
DOI:
https://doi.org/10.29207/joseit.v1i1.1965Keywords:
Computer Vision, Object Detection, YOLO, Object Counting, Degree of Road SaturationAbstract
Identification of road conditions such as congestion at this time still requires manual efforts in getting results. The congestion parameters certainly need to be monitored, especially in crowded areas and big city business centers, such as on Jalan Jendral Sudirman, Central Jakarta. Many parameters to identify the level of road congestion, one of them is by observing the value of the degree of road saturation. The purpose of this study is to propose a system that can calculate the value of the degree of saturation quickly and accurately using a camera. The method that is being proposed is to combine the Computer Vision with YOLO Object Detector techniques based on Deep Learning and the Object Counting method to get the value of traffic flow in the observed area. The results obtained by this system are quite good, this is supported by the error value obtained by the system around 3-4%.
References
Zakaria Moutaki, dkk. 2018. Real-Time System Based on Feature Extraction for Vehicle Detection and Classification. Transport and Telecommunication. 19(2): 93-102.
Margit Betke, dkk. 2000. Real-time multiple vehicle detection and tracking from a moving vehicle. 12:69-83.
Mrs. P. M. Daigavane, Dr. P. R. Bajaj. 2010. Real-time vehicle detection and counting method for unsupervised traffic video on highways.
Uma Nagaraj, dkk. 2013. Traffic jam detection using image processing. 3(2):1087-1091.
M. Sujatha, dkk. 2017. Traffic congestion monitoring using image processing and intimation of waiting time. 115(7):239-245.
Yoichiro Iwasaki. 1998. An image processing system to measure vehicular queues and an adaptive traffic signal control by using the information of the queues. In Intelligent Transportation System, IEEE Conferenc. Pages 195-200.
M. Fathy and M. Siyal. 1998. A window-based image processing technique for quantitative and qualitative analysis of road parameters. IEEE Transportation on Vehicular Technology. 47(4):1342-1349.
Li Wei, Dai Hong-ying. 2016. Real-time road congestion detection based on image texture analysis. Procedia Engineering. 137:196-201.
Huang, L., et al., 2014. Research on road congestion state detection based on machine vision. Journal of China Computer Systems, 35(1), 148-153.
Wang, C., 2010. Research of urban traffic congestion detection based on video processing. Ph.D. thesis, Chongqing University, Chongqing, China.
Yu Qiao, Zhongke Shi. 2012. Traffic Parameters Detection Using Edge and Texture. Procedia Engineering. 29:3858-3862.
Redmon, Joseph., Santosh Divvala., Ross Girshick., Ali Farhadi. 2016. “You Only Look Once: Unified, Real-Time Object Detection”.
Direktorat Jendral Bina Marga. 1997. Manual Kapasitas Jalan Indonesia. Jakarta.
https://devtrik.com/opencv/mengenal-opencv-open-source-computer-vision-library/.
Dr. Adrian Rosebrock, “Deep Learning for Computer Vision with Python”, September 2017.
Jennie Kusumaningrum. 2011. Analisa Perbandingan Kinerja Jalan Jendral Sudirman Sebelum dan Sesudah Perpanjangan Waktu Three In One dan Penerapan Busway. Dinamika Teknik Sipil. 11(1) : 70-80.
Wahban Al Okaishi,a dkk. 2019. Vehicular Queue Length Measurement Based On Edge Detection and Vehicle Feature Extraction. Journal of Theoretical and Applied Information Technology. 97(5) : 1595-1603.
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