Real Time Road Lane Detection and Vehicle Detection on YOLOv8 with Interactive Deployment

Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicle detection. For road lane detection, this study introduces a new proposal through the combination of YOLOv8 (You Only Look Once), and YOLO for vehicle detection, creating a complete package for road understanding and navigation. To achieve accurate road lane detection, YOLOv8, which is the fastest and most accurate model to date, is used to ensure that it can keep track of these lanes in real-time with consistent accuracy whenever the camera is placed under various scenarios with insufficient lighting and occlusions as well. At the same time it uses YOLO for vehicle detection, which improves the recognition and interpretation of the presence and motion of vehicles in real time. The system uses Streamlit, which is an open-source app framework for Machine Learning and Data Science projects to provide an intuitive user experience. Finally, we build an interface to provide results of lane and vehicle detection in realtime manner to allow ease of monitoring and evaluation on the overall system. The combination of YOLOv8 and YOLO with Streamlit provides a powerful, scalable, and deployable solution for practical computer vision applications. Combining well established object detection algorithms with a simplified deployment platform, the proposed system tackles some of the hardest and time consuming areas of autonomous driving. The goal is to make significant contributions to both the safety and efficiency of autonomous vehicles and pave the way for further inevitable advances in ADAS.