  This study presents a computer vision approach aimed at detecting snow on
sidewalks and pavements to reduce winter-related fall injuries, especially
among elderly and visually impaired individuals. Leveraging fine-tuned VGG-19
and ResNet50 convolutional neural networks (CNNs), the research focuses on
identifying snow presence in pavement images. The dataset comprises 98 images
evenly split between snowy and snow-free conditions, evaluated with a separate
test set using the F1 score and accuracy metrics. This work builds upon
existing research by employing fine-tuned CNN architectures to accurately
detect snow on pavements from smartphone-captured images. The methodology
incorporates transfer learning and model ensembling techniques to integrate the
best predictions from both the VGG19 and ResNet50 architectures. The study
yields accuracy and F1 scores of 81.8% and 81.7%, respectively, showcasing the
potential of computer vision in addressing winter-related hazards for
vulnerable populations.
