Abstract
Treacherous driving conditions, especially accumulated snow on roads and bridges, pose a serious safety concern during the winter season. Developing accurate warning systems to classify the severity of snow accumulation in severe weather is crucial. To address this problem, this study investigated the severity of snow accumulation on roads using computer vision techniques. After discussing and analyzing the advantages and disadvantages of the DeepLabV3+ and U-Net models, this paper proposed a classification model for road snow severity based on the fusion of these two models in complex backgrounds. The recognition segmentation task was divided into two stages, with Inverse Perspective Mapping (IPM) added to the output stage of both models. Compared to traditional single-stage models, Pixel Accuracy improved from 76.88% to 97.44%, Mean Intersection over Union from 78.36% to 95.57%, and Class-wise Mean Pixel Accuracy from 79.57% to 96.01%.
Published Version
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