FAST DETECTION OF SEATBELT DRIVER BASED ON IMAGE CAPTURING
Abstrak
Abstract: Traffic accidents are one of the biggest contributors to injuries and fatalities worldwide. Victims of traffic accidents range from minor injuries to severe injuries and even death. The severity of many accidents is often due to a lack of discipline and public awareness of traffic rules and safety measures. Car manufacturers have attempted to mitigate the effects of accidents by providing seat belts. However, many people neglect to use them, thinking that nothing will happen while driving. Even with fines imposed by authorities, people can outsmart them by removing their seat belts when officers are not around. To address this issue, a model has been developed to monitor drivers using artificial intelligence and computer vision. The camera captures images, which are then processed by a neural network trained with the YOLOv5 algorithm. The model has an average precision of 89% and a recall of 81%, and can accurately detect whether drivers are wearing seat belts or not. This model is expected to aid in improving driver and passenger safety on the roads. By paying attention to the use of seat belts, the severity of injuries sustained in accidents can be reduced.
Keywords: computer vision; neural network; seatbelt detection; yolo
Abstrak: Kecelakaan lalu lintas merupakan salah satu penyumbang terbesar cedera dan kematian di seluruh dunia. Korban kecelakaan lalu lintas tidak hanya mengalami cedera ringan, tetapi juga cedera berat bahkan kematian. Parahnya banyak kecelakaan yang terjadi dapat disebabkan oleh kurangnya disiplin dan kesadaran masyarakat akan aturan lalu lintas serta tindakan keselamatan. Produsen mobil telah berusaha untuk mengurangi efek kecelakaan dengan menyediakan sabuk pengaman. Sayangnya, masih banyak orang yang mengabaikan penggunaannya, dengan menganggap bahwa tidak akan terjadi apa-apa saat mengemudi. Meskipun ada denda yang diberlakukan oleh pihak berwenang, orang masih dapat melepaskan sabuk pengaman ketika tidak ada petugas di sekitar. Untuk mengatasi masalah ini, sebuah model telah dikembangkan untuk memantau pengemudi menggunakan kecerdasan buatan dan visi komputer. Kamera mengambil gambar yang kemudian diproses oleh jaringan saraf yang dilatih dengan algoritma YOLOv5. Model ini memiliki presisi rata-rata sebesar 89% dan recall sebesar 81%, dan dapat dengan akurat mendeteksi apakah pengemudi menggunakan sabuk pengaman atau tidak. Model ini diharapkan dapat membantu dalam menangani masalah keselamatan pengemudi dan penumpang di jalan raya. Dengan memperhatikan penggunaan sabuk pengaman, dapat mengurangi tingkat keparahan cedera yang terjadi dalam kecelakaan.
Kata kunci: computer vision; deteksi sabuk pengaman; neural network; yolo
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