The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new YOLO models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11), but these models have not been extensively evaluated in the context of smart parking systems, particularly when combined with Region of Interest (ROI) selection for object detection. Existing methods still rely on fixed polygonal ROI selections or simple pixel-based modifications, which limit flexibility and precision. This work introduces a novel approach that integrates Internet of Things, Edge Computing, and Deep Learning concepts, by using the latest YOLO models for vehicle detection. By exploring both edge and cloud computing, it was found that inference times on edge devices ranged from 1 to 92 seconds, depending on the hardware and model version. Additionally, a new pixel-wise post-processing ROI selection method is proposed for accurately identifying regions of interest to count vehicles in parking lot images. The proposed system achieved 99.68% balanced accuracy on a custom dataset of 3,484 images, offering a cost-effective smart parking solution that ensures precise vehicle detection while preserving data privacy

The Sharjah City Municipality announced its partnership with digital powerhouse Evoteq, an innovative artificial intelligence solution that will enable a driver to book parking through their phone. This initiative will manifest the ability of the AI to help ease the parking dilemmas in Sharjah, as part of their agreement, both parties will be researching for smart transport platform solutions for driver behavior, traffic congestion, and parking. Jihad Tayara, CEO of Evoteq, said, "We are honoured to partner with the Sharjah City Municipality to provide its leading expertise in digital transformation and new ICT technology. As a trusted partner, we are developing solutions that will establish Sharjah as a leading digital economy and smart city." When fully digitize, "Sharjah Smart Parking" will allow car owners and drivers to identify a reserve space in censored parking lots through their phones.

When we think of AI, our mind immediately forms an image of Jarvis from the Iron Man movie, an AI assistant capable of performing a lot of different things. However, the application of AI does not always have to be so complicated. Differentiating a cat image from a dog image, detecting faces in a given image, or counting the number of particular objects in any given image are all applications of AI. One such real-world application is in smart parking systems where the AI detected parking spaces in a given image and determined if a given parking space is available or occupied using a video feed. Such a system is used as an alternative to barrier sensors, infrared sensors and ground sensors, where installation and maintenance costs turn out to be very expensive.

The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. While the deployment of sensor technologies continues to be core to the development of smart parking, a wide variety of other technology innovations are also enabling more adaptable systems--including cameras, wireless communications, data analytics, induction loops, smart parking meters, and advanced algorithms. The future of the smart parking market is expected to be significantly influenced by the arrival of automated vehicles (AVs). Several cities around the world are already beginning to trial self-parking vehicles, specialized AV parking lots, and robotic parking valets. For example, in Boulder, Colorado, ParkPlus is working on deploying the first fully automated parking garage in the Western United States through Boulder's PearlWest mixed-use development.