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The purpose of this study is to successfully train our vehicle detector usingR-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and tooptimize the success rate of the trained detector by providing efficientresults for vehicle detection by testing the trained vehicle detector on thetest data. The working method consists of six main stages. These arerespectively; loading the data set, the design of the convolutional neuralnetwork, configuration of training options, training of the Faster R-CNN objectdetector and evaluation of trained detector. In addition, in the scope of thestudy, Faster R-CNN, R-CNN deep learning methods were mentioned andexperimental analysis comparisons were made with the results obtained fromvehicle detection.

A Vehicle Detection Approach using Deep Learning Methodologies