Deep Learning Based Vehicle Make-Model Classification

• Published in: arXiv.org 2019-02
• Main Authors: Satar, Burak, Dirik, Ahmet Emir
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Annotations; Artificial neural networks; Classification; Deep learning; Ground truth; Image classification; Image detection; Pipelines; Vehicle identification; Vehicles
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1809.00953

This paper studies the problems 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. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.