Real time lane detection using CNN

Road lane detection plays a crucial role in advancing autonomous driving technologies, enhancing vehicle safety, and contributing to intelligent transportation systems. This paper proposes a road lane detection system utilizing Convolutional Neural Networks (CNNs) as a robust and effective solution. The CNN architecture is employed to automatically learn and extract relevant features from road images, enabling accurate identification of lane boundaries. The model is trained on a diverse dataset encompassing various road and environmental conditions to ensure adaptability to real-world scenarios.The proposed CNN-based lane detection system uses transfer learning and is evaluated through comprehensive experiments, measuring its performance in terms of accuracy and efficiency. Comparative analyses with existing methods demonstrate the superiority of the CNN approach, showcasing its ability to deliver precise lane detection results in real-time applications. The study highlights the potential of CNNs in addressing challenges related to road lane detection, paving the way for the integration of advanced computer vision techniques in autonomous vehicles and intelligent transportation systems.