In this paper, we present a novel and effective approach to truck and trailer classification, which integrates deep learning models and conventional image processing and computer vision techniques. The developed method groups trucks into subcategories by carefully examining the truck classes and identifying key geometric features for discriminating truck and trailer types. We also present three discriminating features that involve shape, texture, and semantic information to identify trailer types. Experimental results demonstrate that the developed hybrid approach can achieve high accuracy with limited training data, where the vanilla deep learning approaches show moderate performance due to over-fitting and poor generalization. Additionally, the models generated are human-understandable.


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    Title :

    Truck and Trailer Classification With Deep Learning Based Geometric Features


    Contributors:
    He, Pan (author) / Wu, Aotian (author) / Huang, Xiaohui (author) / Scott, Jerry (author) / Rangarajan, Anand (author) / Ranka, Sanjay (author)


    Publication date :

    2021-12-01


    Size :

    2129756 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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