ML · Done
Road Line Detection
Overview
This project developed a model to detect road lane markings from image and video inputs at the Samsung Innovation Campus Batch 5 competition. The system was deployed using Streamlit, providing an interactive web interface for users to upload inputs and visualize detected lane lines in image and video processing, with potential applications in navigation and autonomous driving systems.
Challenge
Key challenges included handling varying conditions of image/video quality, developing a robust algorithm to detect different types of lane markings, and ensuring the Streamlit application was responsive and efficient for processing.
Result
The system successfully detected road lane markings from images and videos, with results visualized through an interactive Streamlit interface. Users could upload inputs and view detected lane lines with clear overlays, demonstrating potential for navigation systems or autonomous vehicle support.
Key Statistics
Images (JPEG, PNG), Videos (MP4)
Supported Input Types
Image analysis
Frame Analysis
Generate results for videos
Video Detection Analysis
Successful on Streamlit
Deployment Status
Technologies
Computer Vision
Data Processing
Visualization
Deployment
Gallery
Road Line Detection Output
Visualization of detected lane lines on an input image.
IOT Devive
IOT Device for Model Implementation.