Research on target detection method of distracted driving behavior based on improved YOLOv8

TL;DR

This paper presents an enhanced YOLOv8-based method for real-time detection of distracted driving behaviors, improving accuracy and efficiency through model modifications.

Contribution

The study introduces an improved YOLOv8 model incorporating BoTNet, GAM attention, and EIoU loss, optimizing feature extraction and fusion for better performance.

Findings

Detection accuracy of 99.4% achieved

Model size reduced for easier deployment

Real-time detection and classification demonstrated

Abstract

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.