As the population of cities grows and the need for transportation increases, urban parking congestion is becoming a bigger issue. The study highlights the need for smart parking systems to provide on-demand parking solution that reduces traffic and inconvenience. In order to solve the common issue of parking space scarcity in large cities, the study presents a novel solution that is based on real-time slot recognition and car identification. Current parking systems that rely on manual procedures waste time and energy consumption. The proposed method uses Radio-frequency identification (RFID) technology to authenticate cars and prioritize suitable parking spaces, aiming to reduce parking congestion and improve efficiency. In order to streamline parking procedures and maximize resource utilization, this technique attempts to keep an eye on entry and exit timings and notify security personnel of vehicles parked for extended periods of time. Additionally, to find parking spaces for cars in real-time, the study uses the Faster R-CNN (Region Convolutional Neural Network) algorithm, which is well-known for its quick object detection abilities. A crucial gap in smart parking solutions is filled by the research by incorporating sophisticated computer vision-based artificial intelligence into parking systems. This strategy to mitigate urban parking difficulties is shown feasible and effective, as evidenced by the successful testing of the implemented prototype of the suggested system in a university parking space.

As the population of cities grows and the need for transportation increases, urban parking congestion is becoming a bigger issue. The study highlights the need for smart parking systems to provide on-demand parking solution that reduces traffic and inconvenience. In order to solve the common issue of parking space scarcity in large cities, the study presents a novel solution that is based on real-time slot recognition and car identification. Current parking systems that rely on manual procedures waste time and energy consumption. The proposed method uses Radio-frequency identification (RFID) technology to authenticate cars and prioritize suitable parking spaces, aiming to reduce parking congestion and improve efficiency. In order to streamline parking procedures and maximize resource utilization, this technique attempts to keep an eye on entry and exit timings and notify security personnel of vehicles parked for extended periods of time. Additionally, to find parking spaces for cars in real-time, the study uses the Faster R-CNN (Region Convolutional Neural Network) algorithm, which is well-known for its quick object detection abilities. A crucial gap in smart parking solutions is filled by the research by incorporating sophisticated computer vision-based artificial intelligence into parking systems. This strategy to mitigate urban parking difficulties is shown feasible and effective, as evidenced by the successful testing of the implemented prototype of the suggested system in a university parking space.