Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning

Abstract

Black ice has recently been identified as a major cause of transportation accidents due to detecting difficulties on the road surface. It is crucial to provide traffic users with black ice warnings beforehand to sustain commuting safety. The identification of black ice, however, is a difficult initiative, since it necessitates the installation of sophisticated monitoring stations and demands frequently manual inspection. In order to build an economical automatic black ice detection technique, the datasets are built upon a variety of weather conditions, including clear, snowy, rainy, and foggy conditions, as well as two distinct forms of pavement: asphalt and concrete pavement. The Mask R-CNN model was performed to construct the black ice detection via image segmentation. The deep learning architecture was constructed based on pre-trained convolutional neural network models (ResNetV2) for black ice detection purposes. Different pretrained models and architecture (Yolov4) were then compared to determine which is superior for image segmentation of black ice. Afterward, through the retrieved bounding box data, the degree of danger area is determined based on the number of segmentation pixels. In general, the training results confirm the feasibility of the black ice detection method via the deep learning technique. Within “Clear” weather conditions, the detecting precision can be achieved up to 92.5%. The results also show that the increase in the number of weather types leads to a noticeable reduction in the training precision. Overall, the proposed image segmentation method is capable of real-time detection and can caution commuters of black ice in advance.