Copyright © 2025 Authors retain the copyright of this article. This article 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.

@article{170251,
        author = {Amruta Kulkarni and Ameya Chauhan and Pritam Bhokare and Chirag Thawale},
        title = {Intelligent Driver Assistance through AI-based Lane and Object Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3261-3264},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170251},
        abstract = {Drivers rely on visual cues for navigation, with road lines serving as constant guides for steering. A key objective of smart-driving vehicles is to automatically detect these lane lines using algorithms. Lane detection, however, remains a complex challenge that has captivated the computer vision community for many years. It is a multi-feature detection problem that both computer vision and machine learning algorithms continue to address with limited effectiveness. We present an image processing method based on the Hough Transform, Canny Edge Detection, and Object Detection. The primary objective is to utilize the Canny edge detection algorithm to identify features, followed by a method to detect lane lines in images or video. Given the inherent imperfections in images, determining the slope and intercept of lines by examining individual pixels presents significant complexity. The Hough Transform is employed to address this challenge, as it facilitates the identification of significant lines and connects discontinuous edge points within an image. Furthermore, Object Detection is implemented to identify vehicles in proximity to the detected lanes.},
        keywords = {Hough Transform, Canny Edge Detection, Object Detection, Feature Detection},
        month = {November},
        }