Optimized real-time parking management framework using deep learning

Today's modern world has seen massive population growth in the past few decades, causing the automobile industry to expand rapidly. The exponential increase has disrupted the seamless flow of traffic globally and has fashioned significant problems such as traffic congestion and its mismanagement. To tackle these issues, some hardware and data-driven-based solutions have been previously proposed. The hardware-based solutions utilize different types of sensors installed at relevant sites to monitor the status of parking slots which makes them less scalable, complex, costly to install and maintain. In contrast, the data-driven solutions utilize the already available infrastructure of surveillance cameras installed at parking lots, thus overcoming the limitations of sensor-based solutions. However, currently only classification-based approaches are prevalent and adopted to monitor the status of the parking slots which makes the systems less generalized for scalability with slower performance. This paper proposes an intelligent parking management system which employs deep learning to alleviate the limitations in the data driven solutions by leveraging the high performance and fast inference capability of YOLO v5 for vehicles detection instead of parking slot classification. The model was evaluated using the PKLot dataset which is a benchmark for identifying the status of the parking lot with state of the art performance achieved having an accuracy of 99.5%. To augment the performance of the algorithm in real-time, a pretrained model of YOLO v5 on MS COCO dataset was employed to detect and assign vacant parking slots and generate vehicle statistics. The performance of the proposed system was evaluated on our custom dataset with an accuracy of 96.8% achieved and a nearly real-time performance of 45 Fps which makes it more efficient, scalable, and generalized.