🛣️ Lane Detection Using Deep Learning

📌 Overview

This project presents a deep learning-based lane detection system designed to accurately identify lane markings in road images and videos.
The primary aim is to assist Advanced Driver Assistance Systems (ADAS) by providing robust lane detection in various driving conditions, enhancing road safety and driver awareness.

🖼 Demo Results

Detected Lane Example 1	Detected Lane Example 2

⚙ How It Works

1. Data Acquisition – Lane detection datasets with annotated lane markings were used.
2. Preprocessing –
   • Resizing frames
   • Normalization
   • Noise reduction and edge enhancement
3. Deep Learning Model –
   • CNN-based semantic segmentation
   • Trained on annotated lane images to generate binary lane masks
4. Post-processing –
   • Lane contour extraction
   • Overlay of detected lanes on the original frame
5. Output – Continuous real-time lane marking visualization.

🛠 Tech Stack

• Python – Programming language
• OpenCV – Image and video processing
• TensorFlow / Keras – Deep learning framework
• NumPy / Pandas – Data manipulation
• Matplotlib – Visualization