A driver distraction behavior detection method and system based on improved YOLOv8 model

By improving the YOLOv8 model, combining LarK module, CAFM and RT-DETR Decoder, the accuracy and real-time problems of driver distraction behavior detection are solved, and efficient and accurate distraction behavior recognition is achieved and traffic accident risk is reduced.

CN120107937BActive Publication Date: 2025-08-19GUILIN UNIV OF ELECTRONIC TECH +1
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Patent Information

Application Number
CN202510276856.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-03-10
Publication Date
2025-08-19
Estimated Expiration
2045-03-10

AI Technical Summary

Technical Problem

The existing driver distraction behavior detection methods are poor in terms of accuracy and real-time balance, and relying on complex models leads to high computing resources.

Method used

Using the improved YOLOv8 model, by replacing the CSPLayer_2Conv module in Backbone as the LarK module, adding the CAFM module in the Neck part, and using RT-DETR Decoder in the Head part, combining deep separation convolution, self-attention mechanism and multi-level iterative optimization to improve feature extraction and detection performance.

Benefits of technology

It improves detection accuracy and real-time performance, reduces computing resource consumption, effectively recognizes driver distracted behavior, and reduces the risk of traffic accidents.

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Abstract

The present invention discloses a driver distraction behavior detection method and system based on an improved YOLOv8 model, belonging to the field of information processing technology. First, based on the State Farm Distracted Driver Detection dataset, normal samples are eliminated to construct a driver distraction behavior dataset, and the driver distraction behavior dataset is preprocessed using Mosaic data enhancement technology. After manual annotation, the driver distraction behavior dataset is divided into a training set and a validation set. Secondly, an improved YOLOv8 model framework is constructed. The CSPLayer_2Conv module in Stage Layer 1, Stage Layer 2, Stage Layer 3, and Stage Layer 4 in the original YOLOv8 network backbone is replaced with a LarK module, a CAFM module is added to the neck part, and the original detection head Decoupled Head is replaced with an RT-DETR Decoder in the head part. Then, the improved YOLOv8 model is trained using the training set, and the model performance is tested using the Driver Distraction Dataset as a test set. Finally, the detection results are evaluated using evaluation indicators. The method and system provided by the present invention effectively resolve the dilemma of balancing accuracy and real-time performance of existing detection methods and reduce the risk of traffic accidents.
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