Parking Slot Vacancy Detection Using Machine Learning

Abstract: Parking congestion is a persistent issue in urban environments, leading to wasted time, fuel, and environmental impact. This paper introduces a 'Parking Slot Vacancy Detection System' that leverages real-time video feeds and machine learning algorithms, specifically Mask- RCNN, to accurately detect vacant and occupied parking slots. Predefined slot mapping, vehicle detection, and real- time processing are combined to provide efficient and scalable solutions. The proposed system minimizes errors caused by lighting variations or overlapping objects, aiming to enhance parking lot utilization and reduce driver frustration