Drowsy driving represents a major contributor to traffic accidents, and the implementation of driver drowsy driving detection systems has been proven to significantly reduce the occurrence of such accidents. Despite the development of numerous drowsy driving detection algorithms, many of them impose specific prerequisites such as the availability of complete facial images, optimal lighting conditions, and the use of RGB images. In our study, we introduce a novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model (MAF). MAF is aimed at significantly enhancing classification performance, especially in scenarios involving partial facial occlusion and low lighting conditions. It accomplishes this by capitalizing on the local feature extraction capabilities provided by multi-attention fusion, thereby enhancing the algorithm's overall robustness. To enhance our dataset, we collected real-world data that includes both occluded and unoccluded faces captured under nighttime and daytime lighting conditions. We conducted a comprehensive series of experiments using both publicly available datasets and our self-built data. The results of these experiments demonstrate that our proposed model achieves an impressive driver drowsiness detection accuracy of 96.8%.

翻译：疲劳驾驶是导致交通事故的主要原因之一，驾驶员疲劳驾驶检测系统的应用已被证实能显著降低此类事故的发生率。尽管已有多种疲劳驾驶检测算法被开发，但许多算法对使用条件存在特定要求，例如需要完整的面部图像、理想的光照条件以及使用RGB图像。本研究提出了一种名为多注意力融合疲劳驾驶检测模型（MAF）的新方法。MAF旨在显著提升分类性能，特别是在面部部分遮挡和低光照条件下。通过利用多注意力融合提供的局部特征提取能力，该方法增强了算法的整体鲁棒性。为扩充数据集，我们采集了包含夜间与日间光照条件下遮挡与非遮挡人脸的真实场景数据。我们利用公开数据集和自建数据开展了全面实验，结果表明所提出的模型实现了高达96.8%的驾驶员疲劳检测准确率。