• Journal of Industrial Convergence
• /
• v.22 no.10
• /
• pp.21-28
• /
• 2024

Personal Mobility devices pose a significant safety risk to users depending on road conditions. With the recent surge in usage, the number of accidents has also increased, highlighting the need for a preventative system. This study aims to design and develop a warning system to enhance the safety of drivers by detecting road obstacles. To achieve this, a dataset is constructed using images of road obstacles, and the YOLOv5 model is trained. The system is based on the Raspberry Pi 4B, which processes video frames captured by a camera in real-time and triggers an LED warning when an obstacle is detected. The Flask framework is used to monitor the obstacle detection status in real time. Additionally, a GPS sensor is utilized to collect the user's location and speed data, and an auditory warning is triggered via a buzzer if the set speed is exceeded. In the future, this system could be expanded to transmit detected road obstacles and GPS information to a server, providing users with real-time road safety information. The results of this study can serve as essential technology for developing such a system.