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Title |
Real-time Road Surface Recognition and Black Ice Prevention System for Asphalt Concrete Pavements using Image Analysis
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Authors |
정회평(Hoe-Pyeong Jeong) ; 송호민(Homin Song) ; 최영철(Young-Cheol Choi) |
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DOI |
https://doi.org/10.11112/jksmi.2024.28.1.82 |
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Keywords |
블랙아이스; 웨이블릿 변환; 아스팔트 포장; 이미지 식별; 딥러닝 Black ice; Wavelet transform; Asphalt pavement; Image classification; Deep learning |
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Abstract |
Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.
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