Artificial Intelligence (AI) Enabled Vehicle Detection and counting Using Deep Learning

Abstract

Urban traffic management is the system for assessing and controlling density of vehicles on road, to avoid traffic congestion. Vehicle counting is an important module it aids in traffic prediction and urban infrastructure planning. In this paper, various Artificial Intelligence tools and their applications for vehicle detection and counting have been analysed and elaborated. For vehicle detection, used deep learning based YOLOv3 algorithm a highly efficient and accurate algorithm for detecting different classes of vehicles. The role of edge-devices, which locally process the data that they are fed with. Application of Big Data and different AI tools like GNN, LSTM, and QCNN was described for vehicle detection and counting. AI is used to predict and detect traffic flow. Big data helps in maintaining an efficient traffic management system. Both combined, contribute in developing a systematic traffic management system. The objective behind using LSTM networks is to prevent the long term dependency problem. QCNN is used for efficient model training. Organizations implementing vehicle counting and detection focus more on the concise sensors and in house development of efficient algorithms in order to have extensive research and development.