Abstract:

Automated Road Lane detection is an important component of modern driver support systems and autonomous vehicles. This paper presents an intensive learning-based approach to accurately detect and detect road lanes in real time. Methods of traditional lane detection methods rely on edge detection and Hough transform techniques, which are often sensitive to environmental conditions such as shadows, lighting and spread. To remove these boundaries, we appoint a firm nerve network (CNN) trained on a large dataset of road images. The proposed model effectively removes lane features and distinguishes them from other road signs and obstacles. We also use a post-processing technique to enhance detected lane structures and improve strength against challenging road conditions. Experimental results show that our intensive teaching approach is far ahead of traditional methods in terms of accuracy, adaptability and computational efficiency. This study highlights the ability to detect lanes for safe and more reliable autonomous driving systems.