Smart Parking Detector using YOLOv8 and Streamlit

In this project, I developed a smart system based on artificial intelligence and computer vision to detect the status of parking spaces—whether occupied or empty—using the YOLOv8 model and image analysis algorithms.

Project Concept:

The system analyzes video or images from cameras in parking spaces, identifies the locations of vehicles (cars, buses, motorcycles), and then calculates the Intersection over Union (IoU) to determine whether the space is occupied or empty.

Main Functions:

Real-time vehicle detection using YOLOv8.

IoU calculations to classify parking spaces.

An interactive interface based on Streamlit for uploading and analyzing images and videos directly.

Supports real-time video processing with an overlay showing occupied and empty spaces.

Results are visually and organized.

Tools used:

Python – YOLOv8 – OpenCV – Streamlit – Pandas

Result:

An intelligent and accurate system that can be implemented within smart city solutions to monitor parking spaces and improve their efficiency in real time.