Deep Learning Based Vehicle Make-Model Classification

TL;DR

This paper introduces a pipeline combining SSD and CNN models for vehicle make-model classification, achieving improved accuracy and reducing annotation time using a new Turkish vehicle database and practical application for unauthorized vehicle detection.

Contribution

The paper presents a novel pipeline integrating SSD and CNN models for fine-grained vehicle classification, along with a new Turkish vehicle database and an application for unauthorized vehicle detection.

Findings

Approximately 4% improvement in classification accuracy over conventional CNN.

Effective use of detected vehicles as ground truth bounding boxes.

Successful implementation of a vehicle detection and classification application.

Abstract

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.