Vehicle Parking Slot Vacancy Detection Using Machine Learning

Urbanization has significantly increased vehicle density in cities, intensifying the demand for intelligent parking systems. This project introduces a real-time Parking Slot Vacancy Detection system utilizing computer vision and the Mask R-CNN model to identify and segment occupied and vacant parking slots from live video feeds. The system defines parking slot boundaries and uses deep learning for instance-level object detection. It provides a user-friendly web interface for real-time monitoring and slot booking. The solution is scalable, adaptable to various environments, and integrates easily with existing surveillance infrastructure—supporting smarter urban mobility and sustainable traffic management.