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Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks

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Abstract

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.

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Author notes

  1. Abdullah Almutairi and Pan He contributed equally to the work.

Authors and Affiliations

  1. Department of Information Science, Kuwait University, Al-Shadadiya, Kuwait

    Abdullah Almutairi

  2. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Pan He, Anand Rangarajan & Sanjay Ranka

Authors
  1. Abdullah Almutairi
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  2. Pan He
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  3. Anand Rangarajan
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  4. Sanjay Ranka
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Correspondence to Abdullah Almutairi.

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Almutairi, A., He, P., Rangarajan, A. et al. Automated Truck Taxonomy Classification Using Deep Convolutional Neural Networks. Int. J. ITS Res. 20, 483–494 (2022). https://doi.org/10.1007/s13177-022-00306-4

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