DETEKSI MATA MENGANTUK DAN MASKER PADA PENGEMUDI MOBIL MENGGUNAKAN EMBEDDED SYSTEM

Kementerian Kesehatan RI (2020) juga mengeluarkan pedoman kesiapsiagaan dalam menghadapi penyebaran COVID-19. Upaya yang dapat dilakukan pada fase pencegahan oleh setiap individu salah satunya adalah Memakai masker, kecelakaan lalu lintas di jalan raya merupakan penyumbang angka kematian terbesar di dunia. Pengemudi yang mengantuk memiliki pola perilaku yang dapat diamati. Beberapa ciri yang dapat diamati antara lain mata yang lebih sering berkedip. sebuah perangkat embedded yang dapat dipilih untuk implementasi deteksi pada lingkungan pengemudi, salah satunya adalah NVIDIA: Jetson Nano yang memiliki ukuran kecil dengan kinerja yang cukup cepat juga dengan daya yang kecil. Maka penelitian ini berusaha untuk mengimplementasikan metode Deep Learning untuk mendeteksi input citra seseorang yang menggunakan masker dan seseorang dengan mata mengantuk. Dari hasil percobaan diperoleh hasil dengan tingkat akurasi sebesar 80% pada embedded system sebesar 80%, deteksi mata kantuk 80% pada embedded system sebesar 75%, deteksi masker dan mata kantuk 80% pada embedded system sebesar 75%

Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention