Publication: Helmet Detection for Motorcycle Riders and Passengers in Siriraj Hospital Area Using Deep Learning
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Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85217420220
Journal Title
2024 6th World Symposium on Artificial Intelligence, WSAI 2024
Start Page
7
End Page
11
Rights Holder(s)
SCOPUS
Bibliographic Citation
2024 6th World Symposium on Artificial Intelligence, WSAI 2024 (2024) , 7-11
Suggested Citation
Saelee T., Viyanon W. Helmet Detection for Motorcycle Riders and Passengers in Siriraj Hospital Area Using Deep Learning. 2024 6th World Symposium on Artificial Intelligence, WSAI 2024 (2024) , 7-11. 11. doi:10.1109/WSAI62426.2024.10828924 Retrieved from: https://hdl.handle.net/20.500.14740/20492
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Abstract
Siriraj Hospital has a large number of patients, some of whom use motorcycles without helmets, leading to serious injuries when accidents occur. To address this issue, the hospital implemented a policy in 2020 requiring everyone in the Siriraj Faculty of Medicine to wear a helmet. However, visual inspection by staff has limitations and is prone to errors, making it difficult to enforce the policy. This research presents a system for detecting helmet use by riders and passengers within Siriraj Hospital. The system uses the YOLOv8 deep learning model for object detection, trained on a custom dataset collected from CCTV cameras. This approach outperforms manual observation and achieves high accuracy, with a precision of 0.842, recall of 0.811, mAP of 0.858, and F1-Score of 0.826 for YOLOv8x and a precision of 0.868, recall of 0.790, mAP of 0.859, and F1-Score of 0.827 for YOLOv8l The system has the potential to improve helmet compliance and reduce motorcycle-related injuries at Siriraj Hospital.