Weather and surface condition detection based on road-side webcams: Application of pre-trained Convolutional Neural Network

Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Providing drivers with real-time weather information is therefore extremely important to ensure safe driving in adverse weather. However, identification of road weather and surface conditions is a challenging task because it requires the deployment of expensive weather stations and often needs manual identification and/or verification. Most of the Department of Transportations (DOTs) in the U.S. have installed roadside webcams mostly for operational awareness. This study leveraged these easily accessible data sources to develop affordable automatic road weather and surface condition detection systems. The developed detection models are focused on three weather conditions; clear, light snow, and heavy snow; as well as three surface conditions: dry, snowy, wet/slushy. Several pre-trained Convolutional Neural Network (CNN) models, including AlexNet, GoogLeNet, and ResNet18, were applied with proper modification via transfer learning to achieve the classification tasks. The best performance was achieved using ResNet18 architecture with an unprecedented overall detection accuracy of 97% for weather detection and 99% for surface condition detection. The proposed study has the potential to provide more accurate and consistent weather information in real-time that can be made readily available to be used by road users and other transportation agencies. The proposed models could also be used to generate temporal and spatial variations of adverse weather for proper optimization of maintenance vehicles’ route and time.