In the pursuit of enhancing smart city infrastructure, computer vision serves as a pivotal element for traffic management, scene understanding, and security applications. This research investigates the performance of the YOLO v9-c and YOLO v9-e object detection models in identifying vehicles under snowy weather conditions, leveraging various data augmentation techniques. The study highlights that, historically, object detection relied on complex, handcrafted features, but deep learning advancements have enabled more efficient and accurate end-to-end learning directly from raw data. Despite these advancements, detecting objects in adverse weather conditions like snow remains challenging, affecting the safety and effectiveness of autonomous systems. The study examines the performance of YOLO v9-c and YOLO v9-e under four different scenarios: no augmentation, snow accumulation, snow overlay, and snow depth mapping. Results indicate that both models achieve their highest precision without augmentation, with YOLO v9-c and YOLO v9-e reaching precisions of 82% and 80%, respectively. However, the snow accumulation method severely impacts detection accuracy, with precision dropping to 36% for YOLO v9-c and 43% for YOLO v9-e. Snow overlay augmentation shows better adaptability, with YOLO v9-c achieving 68% and YOLO v9-e 76% precision. Snow depth mapping results in moderate impacts, with precisions of 59% for YOLO v9-c and 61% for YOLO v9-e. The findings emphasize the importance of careful selection and tuning of augmentation techniques to improve object detection models’ robustness under snowy weather conditions, thereby enhancing the safety and efficiency of autonomous systems. The study suggests a tunned augmentation that helps YOLO v9-c and YOLO v9-e reach precisions of 85% and 83%. Future research should focus more on optimizing augmentation parameters, diversifying training data, and employing domain randomization to further enhance the robustness and generalization capa...