  With the development of deep learning technology, the detection and
classification of distracted driving behaviour requires higher accuracy.
Existing deep learning-based methods are computationally intensive and
parameter redundant, limiting the efficiency and accuracy in practical
applications. To solve this problem, this study proposes an improved YOLOv8
detection method based on the original YOLOv8 model by integrating the BoTNet
module, GAM attention mechanism and EIoU loss function. By optimising the
feature extraction and multi-scale feature fusion strategies, the training and
inference processes are simplified, and the detection accuracy and efficiency
are significantly improved. Experimental results show that the improved model
performs well in both detection speed and accuracy, with an accuracy rate of
99.4%, and the model is smaller and easy to deploy, which is able to identify
and classify distracted driving behaviours in real time, provide timely
warnings, and enhance driving safety.
