# A Vehicle Detection Approach using Deep Learning Methodologies

arXiv: https://arxiv.org/abs/1804.00429 · HTML: https://mlsanity.com/paper/1804.00429/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [MelNet: A Real-Time Deep Learning Algorithm for Object Detection](https://mlsanity.com/paper/2401.17972/) (2024)
- [Machine Learning Based Early Fire Detection System using a Low-Cost Drone](https://mlsanity.com/paper/2101.09362/) (2021)

## Abstract (arXiv)

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection.