A Vehicle Detection Approach using Deep Learning Methodologies

TL;DR

This paper presents a vehicle detection system using deep learning techniques, specifically Faster R-CNN, trained and tested on vehicle datasets to optimize detection success.

Contribution

It introduces an effective vehicle detection approach utilizing Faster R-CNN, including detailed training and evaluation procedures for improved accuracy.

Findings

Faster R-CNN achieved high detection accuracy on vehicle datasets.

Experimental results show optimized detection success rate.

Comparison with other methods highlights the effectiveness of the proposed approach.

Abstract

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.