Trucks are the key transporters of freight. The types of commodities and goods mainly determine the right trailer for carrying them. Furthermore, finding the commodities’ flow is an important task for transportation agencies in better planning freight infrastructure investments and initiating near-term traffic throughput improvements. In this paper, we propose a fine-grained deep learning based truck classification system that can detect and classify the trucks, tractors, and trailers following the Federal Highway Administration’s (FHWA) vehicle schema. We created a large, fine-grained labeled dataset of vehicle images collected from state highways. Experimental results show the high accuracy of our system and visualize the salient features of the trucks that influence classification.
Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 20 , 2 ; 483-494
2022-08-01
12 pages
Article (Journal)
Electronic Resource
English
truck classification , vehicle classification , deep learning , convolutional neural networks , intelligent transportation system Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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