Earticle

초록

영어

Riding without a helmet is one of the leading causes of injuries and deaths in motorcycle accidents. In a country like Vietnam, the number of motorcycles on the roads is very high, making it difficult to monitor and preserve the safety of the riders. This research aims to propose a method for detecting motorcycle riders who are not wearing helmets using videos from surveillance cameras along the roads, which can help enhance law enforcement and manage the safety of riders. The proposed method utilizes the state-of-the-art object detection algorithm YOLOv5 to detect objects such as helmet, non-helmet, and rider. Next, it determines whether or not riders are wearing helmets in the post-processing step. Finally, the results showed that the detection model has an mAP (mean Average Precision) of around 98% and the proposed approach is able to identify the motorcycle riders who are not wearing helmets precisely.

목차

Abstract
Introduction
Proposed Method
YOLOv5-based Object Detection Model
Post-Processing Stage
Results and Discussion
Conclusion
References

키워드

저자

• Saravit Soeng [ Department of Big Data, Chungbuk National University, Cheongju, South Korea ]
• Vungsovanreach Kong [ Department of Big Data, Chungbuk National University, Cheongju, South Korea ]
• Wan-Sup Cho [ Department of Big Data, Chungbuk National University, Cheongju, South Korea ]
• Jae-Sung Kim [ Department of Big Data, Chungbuk National University, Cheongju, South Korea ]
• Tae-Kyung Kim [ Department of Computer Information Technology, Incheon Jaeneung University, Incheon, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658