Designed and deployed a low-cost embedded driving assistance system that recognizes Moroccan traffic signs in real time and provides multilingual voice guidance to help drivers stay informed while reducing visual distraction.
This project focused on developing an affordable, real-time driving assistance system adapted to Moroccan roads.
The system uses computer vision to recognize traffic signs and communicates relevant information through a multilingual voice assistant.
By combining visual recognition with audio feedback, the project aimed to improve traffic-sign awareness while allowing drivers to maintain their attention on the road.
The solution was developed for deployment on embedded hardware, with an emphasis on real-time operation and accessibility.
Shared fields: Computer Vision
Developed an AI-powered visual inspection solution to detect residue on phosphate wagons, using real-time video analysis. The system includes an application for inspection configuration, result analysis, and KPI monitoring.
Shared fields: Computer Vision
Developed an AI-powered solution combining computer vision and OCR to identify railway components and simplify spare-parts searches. The project included data preparation and tools for continuously enriching the component reference dataset.