Real Time lane detection system using Computer Vision techniques with Object Detection

Since accurate lane recognition is the cornerstone of autonomous driving and, in most situations, a matter of life or death, it is essential for a wide range of computer vision applications in driving systems. There is still room to investigate the nuances of lane recognition and strategies for improving its effectiveness, even with the current state of development. In order to identify lane boundaries from video streams. This study presents a dynamic lane detection system with real-time adaptability, integrating cutting-edge techniques such as perspective transformations, polynomial regression, edge detection, gradient analysis, and object detection. Our approach demonstrates encouraging results through dataset testing and evaluation, underscoring its potential for real-time application in autonomous vehicles and advanced driver assistance systems (ADAS).