Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (6): 13-20.doi: 10.6040/j.issn.1672-3961.0.2024.172

• Machine Learning & Data Mining • Previous Articles    

Risky driving behavior detection based on local and global knowledge distillation

LI Kunbiao, YANG Xiaohui*, ZHANG Feng, XU Tao, GUO Qingbei   

  1. LI Kunbiao, YANG Xiaohui*, ZHANG Feng, XU Tao, GUO Qingbei(School of Information Science and Engineering, University of Jinan, Jinan 250022, Shandong, China
  • Published:2025-12-22

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Abstract: To enhance road safety and prevent traffic accidents, a distracted driving behavior detection algorithm based on local and global knowledge distillation(LGD)was proposed. Built upon a knowledge distillation framework, the method introduced a distillation loss function that integrated both local and global features, guiding the student network to effectively learn the discriminative capabilities of the teacher model. While maintaining a lightweight network structure, the approach significantly improved the recognition accuracy of distracted driving behaviors. By effectively facilitating the learning process of the student network, the method enabled it to achieve detection accuracy comparable to that of the teacher model, despite having fewer parameters. Experimental results demonstrated that the proposed method achieved an accuracy of 91.79% with only 31.85 M parameters, highlighting its effectiveness in addressing the problem of distracted driving detection.

Key words: knowledge distillation, student network, distracted driving detection

CLC Number: 

  • TP391
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