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Deep Learning Based Vehicle Make-Model Classification

  • Burak Satar
  • Ahmet Emir Dirik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)

Abstract

This paper studies the problem of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines which detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.

Keywords

Deep learning Vehicle Model Classification CNN ResNet Detection SSD Fraud License plate 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical-Electronics EngineeringUludag UniversityBursaTurkey
  2. 2.Department of Computer EngineeringUludag UniversityBursaTurkey

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