Accelerating Solar PV Site Selection: YOLO-Based Identification of Sound Barriers Along Highways

The exponential growth of the installation of solar photovoltaic systems has been a significant step in the energy transition toward reducing dependence on fossil fuels and mitigating climate change. This growth has raised concerns about land use, particularly in regions where large tracts are allocated to solar farms. Highway infrastructures such as sound barriers occupy large land surfaces which are under-utilized and could therefore contribute to renewable energy generation without increasing the land use. This study proposes the application of the YOLO object detection algorithm to automatically identify and analyse the locations of sound barriers along highways using video or image data, and to estimate the potential energy output from photovoltaic systems installed on these barriers. The model has been trained and tested on sound barriers along Portuguese highways, achieving a mean average precision exceeding 0.84 for YOLOv10 when using training datasets containing more than 600 images. Using the geolocation of the images and the identification of the number of sound barriers from YOLO, it is possible to estimate the potential generation of electricity and inform decision makers on the technical–economic feasibility of using this infrastructure for energy generation.