EFFICIENCY IMPROVEMENT OF WINTER ROAD SURFACE INTERPRETATION BY USING ARTIFICIAL INTELLIGENCE MODEL
2020 Volume 1 Issue J1 Pages 210-216
Details
2020 Volume 1 Issue J1 Pages 210-216
In recent years, even in areas where there is usually little snowfall, large-scale retention on roads is observed when snowfall occurs. The monitoring of abnormal situations by road administrators and the interpretation of road surface conditions are mainly performed visually, and the efficiency of abnormality detection is a little bit low.
In this study, as a support tool for road administrators to quickly detect anomalies and make processing decisions. We developed an AI model that automatically determines the road surface condition. The training data were made by the image of the dashcam data, which is classified into 5 types, such as dry, wet, flood, wet snow, and consolidation. As a result of automatically discriminating 26199 road surface images of day and night using the AI model, the Training Accuracy rate was around 85%.
