This amazing project combines the power of ASP.NET with the intelligence of Machine Learning to create a traffic sign detection and recognition system. It not only demonstrates the intersection between modern web development and AI but also highlights how we can automate processes and increase efficiency in recognizing visual patterns in urban environments.
- Automatic Traffic Sign Detection: Using advanced computer vision techniques, the system can identify different types of traffic signs in images.
- Real-Time Sign Recognition: Leverage the power of ML not only to detect but also to interpret the meaning of each sign, providing quick and accurate results.
- Intuitive Web Interface: Built with ASP.NET, the user interface is simple and functional, allowing for image uploads and direct viewing of results in the browser.
Demonstrative Image
- ASP.NET Core: A robust foundation for web development, offering performance and security to deliver an efficient application.
- Machine Learning .NET (ML.NET): The heart of the sign recognition system, with trained models for detecting visual patterns.
- Bootstrap: For a responsive and visually appealing user interface.
- Image Upload: The user uploads an image containing one or more traffic signs.
- Image Processing: The system uses Machine Learning models to detect and classify the signs in the image.
- Displaying Results: The web interface displays the results, with identified signs and their respective meanings.
This project was developed to demonstrate how it is possible to integrate cutting-edge technologies like Machine Learning and ASP.NET to solve real-world problems. Whether for applications in traffic systems, security, or automation, this solution offers a glimpse into the future of artificial intelligence in image analysis.
Additionally, it served as study material to learn practically about using Machine Learning and the ML.NET library.
- Easy Training and Adjustments: Possibilities to train and adjust the model with new data to increase accuracy and expand the number of recognized signs. (Idea in progress...)