Vol. 15 No. 2 (2025): IJCRT, Volume 15, Issue 2, 2025
Journal Article

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

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  • Pranesh Kulkarni,
  • Poornima Raikar
Pranesh Kulkarni
Assistant Professor KLS VDIT, Haliyal
Poornima Raikar
Assistat Professor KLS VDIT, Haliyal
DOI: https://doi.org/10.61359/2024050048

Published 2025-02-07

Keywords

  • CNN,
  • YOLOV5,
  • Car Lane,
  • Autonomous Vehicle

How to Cite

Pranesh Kulkarni, & Poornima Raikar. (2025). Real-Time Car lane Detection Using Convolution Neural Networks (CNN). IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms, 15(2), 50385–50390. https://doi.org/10.61359/2024050048

Copyright (c) 2025 IJCRT Research Journal | UGC Approved and UGC Care Journal | Scopus Indexed Journal Norms

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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.

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