  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.
