Vehicle Detection, Counting, and Classification System based on Video using Deep learning Models

Tara Saber Ismail; Abbas M. Ali

doi:10.21271/ZJPAS.36.1.3

Authors

Tara Saber Ismail Department of Software Engineering and Informatics, College of Engineering, Salahaddin University- Erbil, Kurdistan Region, Iraq
Abbas M. Ali Department of Software Engineering and Informatics, College of Engineering, Salahaddin University- Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.21271/ZJPAS.36.1.3

Keywords:

Deep Learning, Yolov5, SSD, Mask R-CNN, DeepSORT, Traffic analysis

Abstract

Traffic analysis is one of the crucial tasks of intelligent transport system that utilizes deep learning for range of purposes. Many tasks, such as vehicle recognition, vehicle counting, traffic violation monitoring, vehicle speed monitoring, vehicle density and so on, can be accomplished by using cameras installed in strategic locations along roads. In this paper powerful deep learning techniques such as (Yolov5, Mask R-CNN, SSD) and state-of-the-art object tracking algorithm known as DeepSORT was used to perform real time vehicle detection and counting in a video. A new highway vehicle detection dataset with overall of 32,265, instances of four vehicle classes named: bus, car, motorbike, truck was created in this paper and utilized for training vehicle detection and counting system. Result shows that average counting accuracy by using Yolov5 combined DeepSORT reaches to 95% while reaches to 91% by using Mask R-CNN combined DeepSORT and 84% by using SSD combined DeepSORT in hard environment. From the experimental work, counting accuracy by using Yolov5 outperforms other two deep learning techniques.

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