基于斯温变换器的高度鉴别驾驶员分心检测方法

Vehicles Pub Date : 2024-01-10 DOI:10.3390/vehicles6010006
Ziyang Zhang, Lie Yang, Chen Lv
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引用次数: 0

摘要

驾驶员分心检测不仅有助于提高道路安全、预防交通事故,还能促进智能交通系统的发展,对创造更安全、更高效的交通环境具有重要意义。由于深度学习算法具有很强的特征学习能力,近年来出现了越来越多基于深度学习的驾驶员分心检测方法。然而,现有的基于深度学习的方法大多仅通过分类损失的约束进行优化,难以获得具有高辨别力的特征,因此这些方法的性能非常有限。本文为了提高不同类别样本特征之间的判别能力,提出了一种高判别特征学习策略,并设计了基于Swin变换器和高判别特征学习策略(ST-HDFL)的驾驶员分心检测模型。首先,通过 Swin Transformer 强大的特征学习能力提取输入样本的特征。然后,通过样本中心距离损失(SC loss)的约束来减小特征空间中同类样本的类内距离,并通过中心向量移动策略来增大不同类样本的类间距离,从而大大提高特征空间中不同类样本的判别能力。最后,我们在 AUC-DD 和 State-Farm 两个公开数据集上进行了大量实验,以证明所提方法的有效性。实验结果表明,与 Drive-Net、MobileVGG、Vanilla CNN 等许多最先进的方法相比,我们的方法能取得更好的性能。
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Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer
Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on.
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