The Advanced Lane Detection is a computer vision–based project designed to detect and track road lane markings using image processing and classical computer vision techniques. The system processes road images and video streams to identify lane boundaries in real time.
This project demonstrates foundational concepts in:
- Image preprocessing
- Feature extraction
- Perspective transformation
- Polynomial curve fitting
- Real-time video processing
It serves as a practical introduction to intelligent transportation systems and autonomous driving technologies.
- Detect lane lines from road images and videos
- Apply image filtering and edge detection techniques
- Perform perspective (bird’s-eye view) transformation
- Fit polynomial curves to lane boundaries
- Overlay detected lanes onto the original video frames
- Generate an output video with detected lanes
- Python
- OpenCV
- NumPy
- MoviePy
- Matplotlib
- Detected lane boundaries visualized on each video frame
- Curvature estimation of lane lines
- Vehicle position relative to lane center
- Output video file generated automatically
Lane detection is a critical component of:
- Advanced Driver Assistance Systems (ADAS)
- Autonomous vehicles
- Intelligent transportation systems
Future research directions include:
- Deep learning–based lane detection (e.g., CNN-based segmentation models)
- Robust detection under adverse conditions (night, rain, shadows)
- Real-time embedded system optimization
- Integration with object detection and path planning systems
This project builds a strong foundation for further research in:
- Artificial Intelligence
- Computer Vision
- Robotics
- Autonomous Systems
Such research may contribute to academic publications, graduate-level study, and industry applications in intelligent mobility.
HOSEN ARAFAT
Software Engineer, China
GitHub: https://github.com/arafathosense
Researcher: Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Image Processing