Driver distraction and fatigue detection in images using ME-YOLOv8 algorithm

Driving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real-time processing possibility especially when combined with the variations of driving environment. This paper introduces “ME-YOLOv8” which operates driver`s distraction and fatigue through a modified version of YOLOv8, which includes modules multi-head self-attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME-YOLOv8 and demonstrate its effectiveness in real-time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state-of-the-art deep learning algorithms play in lowering the risks associated with distracted and tired driving.