Framework for Vehicle Make and Model Recognition—A New Large-Scale Dataset and an Efficient Two-Branch–Two-Stage Deep Learning Architecture

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5 Citaten (Scopus)

Samenvatting

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B–2S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides (Formula presented.) accuracy over the more diverse DVMM dataset and (Formula presented.) accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs.

Originele taal-2English
Artikelnummer8439
Pagina's (van-tot)1-21
Aantal pagina's21
TijdschriftSensors
Volume22
Nummer van het tijdschrift21
DOI's
StatusPublished - 2 nov 2022

Bibliografische nota

Funding Information:
The research work was funded by Innoviris within the research project DRIvINg, and by the Research Foundation—Flanders (FWO) within the research project G094122N.

Publisher Copyright:
© 2022 by the authors.

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

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