DS-Model: Dual-Stream Driver Distraction Detection Model Based on Guiding Learning

Accurate identification of distracted driving behaviour is essential for improving driving safety. At present, deep learning models such as convolutional neural networks (CNNs) and the vision transformer (ViT) are widely used in driving action recognition. However, balancing the number of network parameters, detection accuracy, and real-time performance is difficult. To overcome this problem, a dual-stream driver distraction driving detection model (DS-Model) that is based on a guiding learning framework is proposed in this paper. The guiding network stream is a pretrained model with prior knowledge that can quickly extract effective features. The learning network stream is a multiscale weight calculation module that optimizes feature selection through weight calculation. The output features of the network layer guide the training of the learning network, which effectively improves the training efficiency and prediction accuracy of the model. Moreover, the DS-Model can be naturally extended to multiview settings and shows promising performance for multiperspective driver distraction detection on controlled datasets. We conducted experimental evaluations on three public datasets: AUCDD-V1, SFD, and 100-Driver. The results show that the accuracy of the DS-Model on the three datasets reached 95.80%, 99.91%, and 99.91%, respectively. With only 6.30M parameters, the proposed model outperforms most previous approaches.