Guided View Approach for Lane Detection and Caution Analysis using Computer Vision and Deep Learning Techniques

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution Measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution Measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.