Road traffic Detection using RCNN with MATLAB code|Car Detection

MATLABSolutions demonstrate how to use the MATLAB software for simulation of 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, evaluation of trained detector. These stages and conventional and the faster R-CNN methods will be discussed in this section.

Deep Learning and relevant techonologies are another mostly elaboreted topic in recent periods, as well. If we define deep learning methods that they are the methods that includes artificial neural networks , comprising at least one hidden layer.

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.

Introduction

Today, many new technological developments have occurred. As a result of these technological developments, people may face several cruical problems. Some of the negativities to be experienced with the detection of such problems can be minimized through various approaches.

Because of this; it is necessary to fall in parallel with technological and scientific developments, traffic accidents are still at the forefront people's daily life both in our country and also all over the world. Compared to air, sea and railway traffic, traffic on highways continues to be a significiant problem in current life. Because no human being can not be thought without driving out on a daily basis, traffic accident are considered to be an ordinary event of each human being. For this reason, it is essential for any person who involved in a traffic accident to objectively present the possible defect situations in a technical, legal and scientific way. So, vehicle detection and tracking systems gather lots of attention from the reasearchers and is highly focused on by scientis. Therefore, there are studies have been carried out about about vehicle detection and tracking systems and day after day new solutions and algorithms are developed with new studies

In this study, Road traffic Detection and deep learning approaches are combined. Moreover, our vehicle detector on the sample vehicle data sets are individually and successfully trained using fast RCNN and R-CNN deep learning methods respectively. The trained vehicle detector is tested on the test data and efficient results are obtained from vehicle detection problem. In addition, the success detection rate of the trained detector has been tried to be maximized as much as possible and experimental analysis comparisons are made with the results obtained from the methods.. Overall section will detail the proposed method, wheras will illustrate implemnetaiton details and also the exerimental results. Finally , the study will be concluded insection.