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The implementation of robust lane detection algorithms is pivotal for ensuring road safety.This project introduces a cutting-edge solution leveraging the power of OpenCV to enhance lane detection capabilities.Our approach integrates computer vision techniques to accurately identify and track lane markings in real-time.Our system extracts pertinent information from video feeds by leveraging OpenCV's image processing and computer vision functionalities, which allows for accurate lane detection in a variety of environmental situations.The model's capacity to manage intricate road scenarios, such as difficult lighting conditions and shifting lane markers, is further improved by the incorporation of machine learning.The innovative part of the project is how well OpenCV's powerful libraries are integrated into it, resulting in a scalable and effective lane detecting system.Our research, which harnesses the combined power of OpenCV and computer vision, raises the bar for accurate and flexible lane detection in the ever-changing world of contemporary transportation.
Devi et al. (Sat,) studied this question.
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