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http://dx.doi.org/10.12815/kits.2020.19.6.96

Class 1·3 Vehicle Classification Using Deep Learning and Thermal Image  

Jung, Yoo Seok (Korea Institute of Civil Engineering and Building Technology)
Jung, Do Young (Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.6, 2020 , pp. 96-106 More about this Journal
Abstract
To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.
Keywords
Vehicle classification; Thermal image; Deep learning; CNN; Traffic monitoring;
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