Gerçek Zamanlı Sürücü Yorgunluk Tespiti ve Uyarı Sistemi

Öz

Sürücü yorgunluğu, sürüş sırasında bilişsel performansın azalmasına ve sürücünün tepki zamanının uzamasına sebep olan trafik güvenliğini tehdit eden hem sürücüyü hem de çevresindeki insanları tehlikeye atan önemli etkenlerden biridir. Sürücü yorgunluğu, uzun süreli araç kullanımı ile sürüş anında sürücünün performansını olumsuz etkileyebilir. Bu çalışma sürücünün yorgunluğunu gerçek zamanlı olarak tespit edebilmek amacıyla, Raspberry Pi 4 mikro bilgisayar tabanlı bir sistem tasarımı sunmaktadır. Sistem araç içi kamera ile yüz ve göz hareketlerinin izlenmesi ve MPU6050 ivmeölçer sensörü ile baş hareketlerinin tespiti, analiz edilmesi esasına dayanmaktadır. OpenCV kütüphanesi kullanılarak sürücünün yüzünün algılanması ve göz hareketlerinin tespiti gerçekleştirilmiştir. Görüntü işleme algoritmaları ile göz en-boy oranı(EAR) ve gözün kapalı kalma süresinin yüzdesi(PERCLOS) hesaplanmaktadır. Aynı zamanda MPU6050 sensöründen gelen verilerle sürücünün baş eğimi, başın anlık düşme hareketi ve başın doğal konumundaki hareketleri analiz edilerek sürücünün yorgunluğunu belirlenmesinde destek sistemi olarak çalışmaktadır.

Anahtar Kelimeler

• Gerçek zamanlı sistemler
• Sürücü yorgunluk tespiti
• Görüntü işleme
• Raspberry Pi 4

Real-Time Driver Fatigue Detection and Alert System

Abstract

Driver fatigue is one of the critical factors that threaten traffic safety by impairing cognitive performance and prolonging reaction times during driving, thereby posing risks not only to the driver but also to surrounding individuals. Fatigue can nagatively impact driving performance, particularly during long periods of continuous vehicle operation. This study presents the design of a real-time driver fatigue detection system based on Raspberry Pi 4 microcomputer. The system operates by monitoring facial and ocular movements using an in-vehicle camera and analyzing head movements via an MPU6050 accelerometer sensor. Facial detection and eye tracking are performed using the OpenCV library. Through image processing algorithms, the Eye Aspect Ratio(EAR) and the Percentage of Eye Closure(PERCLOS) are calculated to evaluate visual indicators of fatigue. Simultaneously, data obtained from the MPU6050 sensor is used to analyze head tilt, sudden head drops and deviations from the natural head position. These multimodal data streams work in conjunction to support the detection of driver fatigue, forming the basis of an assistive moniroring system.

Keywords

• Real-Time system
• Driver drowsiness detection
• Image processing
• Raspberry Pi4

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