A data science project which has used many libraries like OpenCv, numPY, Matplotib, etc. In this project we have made a lane detector program which will be used to detect the lane The Road Lane Line Detection System project utilizes state-of-the-art image processing and machine learning techniques to identify and track lane lines on various road surfaces. The system's primary objective is to assist drivers in maintaining their lane position and improving overall road safety. The key features and functionalities of the project include:
Image Acquisition: The system captures real-time video footage using a camera mounted on a vehicle's windshield.
Preprocessing: The acquired video frames undergo preprocessing techniques, such as noise reduction, contrast enhancement, and image normalization, to improve the accuracy of lane line detection.
Lane Line Detection: Using computer vision algorithms, the system detects lane lines by analyzing the preprocessed frames. It employs techniques like edge detection, Hough transform, and line fitting to accurately identify the lane boundaries.
Lane Tracking and Prediction: Once the lane lines are detected, the system tracks their movement over consecutive frames and predicts their trajectory. This information can be used to provide real-time feedback to the driver regarding lane departure or lane position deviation.
Visualization and Alerts: The system overlays the detected lane lines onto the video feed, providing a visual representation of the road's lane configuration. It also generates alerts or warnings to notify the driver of potential lane violations or unsafe driving behavior.
Performance Evaluation: The project includes comprehensive performance evaluation measures to assess the accuracy and robustness of the lane line detection system. It involves testing the system on diverse road conditions, including varying lighting conditions, road markings, and traffic scenarios.