The fatigue driving detection method based on deep learning

This article proposes an improved method for fatigue driving detection using the YOLOv5 algorithm, employing three techniques: Global Attention Mechanism (GAM), MPDIoU loss function, and Receptive Field Block (RFB). The core of this approach is to enhance the YOLOv5 algorithm's ability to recognize driver fatigue status, particularly through more accurate analysis of facial expressions and body language. The Global Attention Mechanism (GAM) weights key regions in the image, improving the algorithm's attention to driver facial features and, therefore, more accurately capturing subtle facial expressions related to fatigue. The MPDIoU loss function further optimizes the algorithm's training process, enhancing the model's ability to locate and recognize complex facial features. The introduction of Receptive Field Blocks (RFB) mimics the processing methods of the human visual system, improving the recognition of key features of fatigue driving behavior, such as eye closure, head position, and posture changes. This method has been tested and validated in various driving scenarios. The results demonstrate that, compared to the traditional YOLOv5 algorithm, this improved approach exhibits higher accuracy and reliability in fatigue driving detection.