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http://dx.doi.org/10.36498/kbigdt.2022.7.1.15

A Study on the Real-time Recognition Methodology for IoT-based Traffic Accidents  

Oh, Sung Hoon (동의대학교 대학원 인공지능학과 부산IT융합부품연구소)
Jeon, Young Jun (동의대학교 부산IT융합부품연구소)
Kwon, Young Woo (동의대학교 대학원 인공지능학과 한국건설생활환경시험연구원)
Jeong, Seok Chan (동의대학교 e비즈니스학과 인공지능그랜드ICT연구센터 부산IT융합부품연구소)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 15-27 More about this Journal
Abstract
In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.
Keywords
IoT(Internet of Thing); Deep learning; Traffic Accident; golden time;
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