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Sensors and Materials, Volume 33, Number 5(1) (2021)
Copyright(C) MYU K.K.
pp. 1517-1530
S&M2551 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3236
Published: May 6, 2021

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model [PDF]

Shin-Hung Pan and Shu-Ching Wang

(Received December 26, 2020; Accepted March 22, 2021)

Keywords: YOLO, deep learning, object detection, freeway, CCTV

This study focuses on object detection in computer vision research. The object detection process often encounters many uncertainties, such as the uncertainty of the number of objects in the image, the different conditions of the objects including their appearance, the current driving speed, the obstruction between vehicles, sunlight in the daytime, the lack of light at night, the irreversible factors related to the CCTV lens, and other factors, which make object detection and image preprocessing difficult. Taiwan’s freeways are all equipped with CCTV to monitor real-time road conditions, and all CCTV images are available to the public via the internet. However, in freeway segments and tunnels, and even on traffic-prone roads, traffic jams and accidents are only judged by “human power.” Therefore, in this study, we use existing CCTV streaming video as a vehicle sensor data source and the You Only Look Once (YOLO) algorithm to perform object detection as well to tune adjustable parameters to achieve the desired results. From the preliminary results of this study, the current model based on the YOLOv3 algorithm and the Common Objects in Context (COCO) image dataset has an accuracy of 44% during the daytime and 41% during the nighttime for CCTV cameras installed outdoors. In the future, we will analyze larger amounts of CCTV video streaming data to detect whether a road is congested and even detect the occurrence of traffic accidents.

Corresponding author: Shu-Ching Wang


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Cite this article
Shin-Hung Pan and Shu-Ching Wang, Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model, Sens. Mater., Vol. 33, No. 5, 2021, p. 1517-1530.



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