Road Lane Line Detection

Abstract

Real-time visual perception is becoming a crucial component in enhancing road safety as smarter transportation systems gain international attention. In order to improve vehicle awareness, this study proposes an integrated vision-based system that combines lane recognition, multi-object tracking, and object detection. In order to promote safer driving, the system processes video input to identify lane boundaries, detect and follow nearby vehicles, and foresee possible hazards. It operates dependably in a variety of road conditions by utilizing both deep learning and conventional computer vision techniques. The model provides a strong basis for advanced driver-assistance and autonomous vehicle technologies, as demonstrated by its consistent real-time output and high accuracy when tested on actual urban driving scenarios.

Introduction

The text describes an end-to-end real-time visual perception system designed to enhance intelligent transportation and autonomous driving. This system integrates lane detection, multi-object detection, and tracking to provide comprehensive situational awareness. Using a front-facing vehicle camera, the system detects lane markings and tracks vehicles and obstacles in various road and weather conditions.

The core technologies include YOLOv3 for object detection, DeepSORT for tracking, and conventional computer vision techniques (Canny edge detection, Hough Transform) for lane detection. The system measures lane curvature and vehicle offset, enabling lane departure warnings and adaptive lane centering. It also includes a collision warning module that analyzes the movement and relative positions of surrounding vehicles to issue proactive alerts.

The system processes video input frame-by-frame in real time, combining detection, tracking, and lane recognition to overlay visual markers and warnings on the video feed. Despite running at about 2.6 frames per second, it demonstrates stable performance and high accuracy in real-world city driving scenarios.

Overall, this integrated approach supports advanced driver assistance systems (ADAS) and autonomous vehicle platforms by improving road safety, traffic monitoring, and enabling proactive decision-making. The modular architecture allows future expansion with additional features like traffic sign recognition or semantic segmentation.

Conclusion

This work offers a strong and efficient solution to real-time road perception with computer vision and deep learning. With YOLOv3 for object detection, DeepSORT for multi-target tracking, and traditional image processing algorithms for detecting lanes, the system exhibits high performance in detecting vehicles, detecting drivable lane regions, and computing driving metrics such as curve radius and center offset.Experimental outcomes on actual urban road video footage validate the system\'s capability to process intricate traffic situations with multiple dynamic objects. The lane is correctly marked, vehicles are continuously tracked with distinct IDs, and the computed offset suggests correct positioning within the lane. Such outputs enable crucial autonomous capabilities like lane keeping, collision avoidance, and decision-making under changing conditions. While the system only operates at a relatively low frame rate of 2.59 FPS, it can still provide consistent real-time insights, thus offering much potential for further development in Autonomous Vehicle technologies and Advanced Driver Assistance Systems (ADAS). Improvements going forward can be made in performance and sensor fusion integration into the technique for its accuracy and scalability to be used in real-time driving scenarios.

References

[1] https://www.nature.com/articles/s41598-024-66913-1 Authors: Youchen Kao, Shengbing Che , Sha Zhou, Shenyi Guo, Xu Zhang &Wanqin Wang Date: July 2024 [2] https://ieeexplore.ieee.org/document/5415547 Authors: Alberto Broggi, Andrea Cappalunga, Claudio Caraffi, Stefano Cattani, Stefano Ghidoni, Paolo Grisleri, Pier Paolo Porta, Matteo Posterli, Paolo Zani. Date: March 2010 [3] https://www.researchgate.net/publication/336255086_Road_Lane-Lines_Detection_in_RealTime_for_Advanced_Driving_Assistance_Systems Authors: Wael A. Farag Date: November 2018. [4] https://www.ijert.org/lane-line-detection-for-vehiclesAuthors: K. Sai Venkata Sri Supriya, Md. Ayesha Begum, M. Lakshma Naik, K. Roopeshnadh Date: May 2022

Copyright

Copyright © 2025 Swapnil Sonawane, Shreyas Panzade, Sakshi Wagh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.