Real-Time Detection of Blowing Snow Events on Rural Mountainous Freeways Using Existing Webcam Infrastructure and Convolutional Neural Networks

The main objective of this study is to automatically detect real-time snow-related road surface conditions using imagery captured from existing roadside webcams along interstate freeways. Blowing snow is considered one of the most hazardous roadway weather phenomena because it significantly reduces driver visibility and adversely affects vehicle operation. A comprehensive image preprocessing and reduction process was conducted to construct two reference datasets. The first dataset consisted of two categories (blowing snow and no blowing snow), while the second dataset included five surface condition categories: blowing snow, dry, slushy, snow covered, and snow patched. Eight pre-trained convolutional neural networks (CNNs), including AlexNet, SqueezeNet, ShuffleNet, ResNet18, GoogleNet, ResNet50, MobileNet-V3, and EfficientNet-B0, were evaluated for roadway surface condition classification. For Dataset 1, ResNet50 achieved the highest detection accuracy of 97.88%, while AlexNet demonstrated competitive performance with 97.56% accuracy and significantly shorter training time. Among the lightweight architectures, MobileNet-V3 achieved 95.56% accuracy, demonstrating strong computational efficiency. EfficientNet-B0 achieved 93.56% accuracy while maintaining reduced model complexity. For Dataset 2, ResNet18 achieved the highest multi-class detection accuracy of 96.10%, while AlexNet required the shortest training time among the evaluated models. A comparative analysis between deep CNN models and traditional machine learning approaches showed that deep CNNs significantly outperform feature-based methods in detecting blowing snow conditions. The proposed framework provides an automated, accurate, and scalable solution for roadway surface condition monitoring and supports real-time applications in intelligent transportation systems.