Real-time Object Detection for Safer Driving Experience in Urban Environment: Leveraging YOLO Algorithm

This project offers a hands-on investigation of object identification utilizing the YOLO method, Python, and OpenCV. It begins by explaining the YOLO architecture, focusing on the single-stage detection process for bounding box prediction and class probability calculation. The setup phase includes library installation and model configuration, resulting in a smooth implementation procedure. Using OpenCV, the project includes preparatory processes required for object detection in images. The YOLO model is seamlessly integrated into the OpenCV framework, enabling object detection. Post-processing techniques, such as non-maximum suppression, are used to modify detection results and improve accuracy. Visualizations, such as bounding boxes and labels, are used to help interpret the discovered items. The project finishes by investigating potential expansions and optimizations, such as custom dataset training and deployment on edge devices, opening up new paths for further investigation and development. This project provides developers with the tools and knowledge they need to build effective object detection systems for a wide range of applications, from surveillance and security to autonomous vehicles and augmented reality.