With the development of society, transportation has become a crucial aspect of our daily lives, resulting in a significant increase in the number of vehicles on the roads. As a result, finding available parking slots in metropolitan areas can be a daunting task, leading to a higher risk of accidents and carbon emissions, and negatively impacting the driver’s health. In this context, technological solutions for parking management and real-time monitoring have become crucial to streamline the parking process in urban areas.

This study proposes a new computer vision-based system that utilizes color imagery processed by an innovative deep learning algorithm to identify vacant parking spaces in challenging scenarios. The system utilizes a multi-branch output neural network that maximizes contextual image information to determine the occupancy of each parking space. Unlike existing approaches that only use a neighborhood around each slot, every output infers the occupancy of a specific parking space using all the input image information. This approach makes the system very robust to changes in illumination conditions, various camera perspectives, and mutual occlusions between parked cars.

An extensive evaluation of the proposed system has been conducted using several public datasets, which has shown that the system outperforms existing approaches. The innovative approach of this system can significantly improve the parking experience for drivers, reduce the risk of accidents and carbon emissions, and positively impact the overall health and well-being of urban communities.

Read full paper in [https://www.mdpi.com/1424-8220/23/6/3329/htm]