Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models
This work addresses driver safety by improving fatigue detection, but it is incremental as it compares existing models without introducing new methods.
This research tackled the problem of detecting driver fatigue by comparing YOLO object detection models, finding that YOLOv8 achieved the best balance of accuracy and speed in real-time detection.
This research delves into the development of a fatigue detection system based on modern object detection algorithms, particularly YOLO (You Only Look Once) models, including YOLOv5, YOLOv6, YOLOv7, and YOLOv8. By comparing the performance of these models, we evaluate their effectiveness in real-time detection of fatigue-related behavior in drivers. The study addresses challenges like environmental variability and detection accuracy and suggests a roadmap for enhancing real-time detection. Experimental results demonstrate that YOLOv8 offers superior performance, balancing accuracy with speed. Data augmentation techniques and model optimization have been key in enhancing system adaptability to various driving conditions.