Real time MultiVehicle Detection Under Diverse Weather Condition

The project Real-Time Multi-Vehicle Detection Under Diverse Weather Conditions focuses on developing an advanced system for detecting various vehicle types—such as cars, buses, and motorcycles—in accurates, even in challenging weather conditions like rain, fog, and snow. The system utilizes the YOLOv9 (You Only Look Once) algorithm, which is known for its speed and accuracy in object detection, making it suitable for real-time applications. The model is trained on a robust dataset from Roboflow, ensuring the algorithm can detect vehicles under different environmental conditions. A user-friendly web interface is built using Streamlit, enabling users to register, log in, and upload images for processing. Once an image is uploaded, the system uses YOLOv9 to analyze the image and provide detection results, including bounding boxes and vehicle classifications. This project is designed to enhance traffic monitoring, smart city infrastructure, and road safety. It is particularly valuable for autonomous driving systems and traffic management solutions, providing reliable vehicle detection even in poor visibility scenarios. By addressing the challenge of detecting vehicles under diverse weather conditions, the project contributes to the development of safer, smarter transportation systems in various environments.

Keywords:

YOLOv9, Multi-Vehicle Detection, Deep Learning, Streamlit, Real-Time Detection, Weather Conditions, Object Detection, Traffic Monitoring, Autonomous Vehicles, Roboflow, Smart Cities