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Due to static traffic management regulations on roadways, traffic flow may become congested as it has been growing on roads. Estimating traffic density impacts intelligent transportation systems as it helps build efficient traffic management. Vehicle recognition and counting are two main steps to estimate traffic density. Vehicle identification systems can use motion, handcrafted features, and convolutional neural network (CNN)-based methods. The utilization of deep learning technologies is increasing daily with the popularity of CNN. Different classification and detection models have been developed using transfer learning. In this study, data are collected from several open-source libraries, including MB7500, KITTI, and FLIR. Image annotation has been done to classify vehicles into different categories. Various data augmentation methods are implemented to increase the dataset size and to reduce class imbalance …