Abstract
Computer vision using deep learning has revolutionized the detection system and vehicle detection is no exception to it. The importance of vehicle classification and detection is in Intelligent Vehicle and Transportation systems which require making critical decisions based on this information; therefore it is a prominent area of work. This paper presents a vehicle detection model predicated on convolution neural network using bounding box annotations for marking the region of interest. The model is tuned to give best performance by evaluating the different parameter configurations. The implementation is done using Python and OpenCV and is trained upon Google Colab’s free GPU access. The paper presents an efficient stepwise explanation of the process flow in detecting cars in various real life scenes. The proposed model is trained on the combination of two benchmark dataset and attains 94.66% accuracy and 95.13% precision.
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