Speaker
Description
The world is facing an urgent challenge: the need for sustainable and clean energy solutions. Highways, with their vast and underutilised infrastructure, present an innovative opportunity through the integration of photovoltaic panels.
In this work, I employed YOLO, a state-of-the-art computer vision algorithm, to automatically detect and identify highway structures and evaluate their potential for solar energy generation. As a first step in detecting sound barriers, I applied data augmentation techniques to artificially expand my dataset, which was then used to train a YOLOv10n model. The model achieved impressive results, with a precision of 86%, a recall of 89%, and a mean average precision (mAP) of 91%, which demonstrates strong performance and highlight the effectiveness of this approach.
This research contributes to the development of smarter and greener highways.
