A paper titled “Image-Based Parking Occupancy Detection Using Deep Learning and Faster R-CNN”, authored by Z. Scekic, S. Cakic, T. Popovic and A. Jakovljevic, was presented at the 26th International Conference on Information Technology, IEEE IT2022. The paper was presented by a young researcher Ms Zoja Scekic, Faculty of Applied Sciences, on 17 February 2022 in the paper presentation session at IT2022. The paper discussed the use of machine learning and HPC to develop prediction models that could be embedded into edge AI setting for Smart parking solutions The effort was supported by the EuroCC Monteengro team.
ABSTRACT – Smart city is one area with the growing use of Internet of Things and Artificial Intelligence. The concept of smart cities relies on making quality of life better, and solving important problems, such as global warming, public health, energy and resources. Smart parking management is one of the smart city use cases. This paper describes the use of deep learning algorithms to process images of parking lots and determine their current occupancy. The development of prediction models was done using PKLot dataset with 12417 images, Detectron2 software library, and Faster R-CNN algorithm. The resulting models can be integrated into parking space sensors and used for building smart parking solutions, and thus lead to more efficient use of space in urban areas, reduced traffic congestion, as well as reducing parking surfing to minimum.
