traffic sign detection
this project implements traffic sign detection and classification algorithms as part of the SY32 course at UTC.
the goal is to develop a system that can reliably identify different types of traffic signs from camera images, including warning signs, speed limits, and traffic lights.
two technical approaches were explored:
- classical computer vision methods: extracting visual features from images and using machine learning models to classify signs.
- deep learning techniques: using artificial neural networks to analyze images in a more complex way.
The work involved experimentation and analysis to understand the strengths and weaknesses of each method, using metrics like precision and recall to measure performance.
example images:
