Citation

Li J, Abe M, Sugisaki K, Nakamura K, Kamiishi I. Intel. Inform. Infrastruct. 2020; 1(J1): 210-216.

Copyright

(Copyright © 2020, Japan Society of Civil Engineers)

DOI

10.11532/jsceiii.1.J1_210

PMID

unavailable

Abstract

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%.

Language: en

Keywords

artificial intelligence; road surface interpretation; winter road managment