Real-Time Car lane Detection Using Convolution Neural Networks (CNN)

Abstract

This paper introduces a novel approach to simultaneously address the challenges of car lane detection and object detection in intelligent transportation systems. The proposed method integrates Convolutional Neural Networks (CNNs), utilizing the robustness of YOLOv5 for object detection and a custom sequential CNN model specifically designed for lane detection. This integrated approach allows the system to concurrently identify lane boundaries and detect various objects of interest, including vehicles, pedestrians, and traffic signs. By handling these tasks simultaneously, the proposed solution enhances the efficiency and effectiveness of autonomous vehicles, contributing to safer and more autonomous driving experiences.