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@article{196576,
        author = {Basina Dhanasri and P.V.S Manisha and P. Santoshi and T.Venu and V. Ajay},
        title = {Smart Road Lane Detection Using Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3655-3664},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196576},
        abstract = {Road Lane detection is a fundamental component of Advanced Driver Assistance Systems (ADAS), as it enables vehicles to maintain proper lane positioning and enhances road safety. However, reliable lane detection in real-world environments remains challenging due to varying lighting conditions, shadows, faded lane markings, curved roads, and weather disturbances. Traditional edge-based methods often fail under such complex scenarios.
 This paper presents a smart road lane detection system using computer This paper presents a smart road lane detection system using computer vision techniques implemented in Python with OpenCV and NumPy. The proposed framework employs a multi-stage image processing pipeline that includes grayscale conversion, Gaussian smoothing, Canny edge detection, region of interest extraction, and perspective transformation to obtain a bird’s-eye view of the road. A sliding window search algorithm is then applied to identify lane pixels, followed by second-order polynomial curve fitting to model lane curvature accurately. The detected lane boundaries are projected back onto the original frame and highlighted with a green safe-driving region overlay. This paper presents a smart road lane detection system using computer vision techniques implemented in Python with OpenCV and NumPy. The proposed framework employs a multi-stage image processing pipeline that includes grayscale conversion, Gaussian smoothing, Canny edge detection, region of interest extraction, and perspective transformation to obtain a bird’s-eye view of the road. A sliding window search algorithm is then applied to identify lane pixels, followed by second-order polynomial curve fitting to model lane curvature accurately. The detected lane boundaries are projected back onto the original frame and highlighted with a green safe-driving region overlay.},
        keywords = {Lane Detection, Computer Vision, Advanced Driver Assistance Systems (ADAS), Perspective Transformation, Sliding Window Algorithm, Polynomial Curve Fitting, OpenCV, Image Processing,Real-Time Video Processing},
        month = {April},
        }