# Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study

arXiv: https://arxiv.org/abs/1601.01145 · HTML: https://mlsanity.com/paper/1601.01145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2016.

## Built on by (most cited first, 3 of 3)

- [Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh](https://mlsanity.com/paper/2212.09144/) (2022)
- [Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms](https://mlsanity.com/paper/2302.00648/) (2023)
- [Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline](https://mlsanity.com/paper/2406.01071/) (2024)

## Abstract (arXiv)

We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions we propose our approach that outperforms state-of-the-art methods, and achieves promising results on image with extreme lighting conditions.