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Developing a Solution to Improve Road Safety Using Multiple Deep Learning Techniques
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- International Journal of Internet, Broadcasting and Communication 바로가기
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- 통권
- Vol.15 No.1 (2023.02)바로가기
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- 페이지
- pp.85-96
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A426055
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.
목차
Abstract
1. Introduction
2. Literature Review
3. Data Collection and Processing
3.1 Data Collection
3.2 Data Processing
4. Model Development
4.1 Weather Classifier
4.2 Spatial Classifier
4.3 Road Classifier
5. Results
6. Conclusions
Acknowledgment
References
키워드
Meteorological Data Orthophoto Image Data Road Surface Data Deep Learning저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- International Journal of Internet, Broadcasting and Communication
- 간기
- 계간
- pISSN
- 2288-4920
- eISSN
- 2288-4939
- 수록기간
- 2009~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / International Journal of Internet, Broadcasting and Communication Vol.15 No.1
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