Published in Scientific Papers. Series E. Land Reclamation, Earth Observation & Surveying, Environmental Engineering, Vol. XIII
Written by Dan Constantin PUCHIANU
Intelligent parking systems that capitalize on the characteristics of deep-learning architectures have become an important point of research due to their ability to improve efficiency, reduce traffic congestion and improve the experience of drivers. The availability and ineffective management of parking spaces is a major problem in most cities, leading to traffic congestion and pollution. This study aims to introduce a state-of-the-art implementation of intelligent parking systems using neural networks and web application development. The optimization of neural networks enables careful analysis of images and videos from surveillance cameras to quickly identify available parking spaces in real time. These implementations offer innovative solutions for drivers looking for parking spaces, thus reducing the time spent searching for them and reducing traffic congestion. The development of a web application was proposed to complete the implementation part of the study using trained neural networks to provide a useful user interface for displaying available parking spaces. This study aims to open the way to automated parking, predictive parking availability and smart parking systems. Compared to other architectures, the proposed models have demonstrated superior performance, and the effective use of these technologies can improve smart-city practices and reduce the impact of traffic pollution on the environment.
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