标题： 基于空间自注意力和联邦学习的隐私保护驾驶员困倦检测 显示英文标题

标题： Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning

摘要： 驾驶疲劳是交通事故的主要原因之一，被公认为导致交通相关死亡的主要因素之一。然而，在现实世界环境中，由于不同个体的面部数据是分散且高度多样的，准确检测疲劳仍然是一项具有挑战性的任务。在本文中，我们提出了一种新的疲劳检测框架，该框架旨在与异构和分散的数据有效配合。我们的方法开发了一种新的空间自注意力（SSA）机制，结合长短期记忆（LSTM）网络，以更好地提取关键面部特征并提高检测性能。为了支持联邦学习，我们采用了一种梯度相似性比较（GSC），在聚合之前从不同操作员中选择最相关的训练模型。这提高了全局模型的准确性和鲁棒性，同时保护了用户隐私。我们还开发了一个定制工具，通过提取帧、检测和裁剪人脸以及应用旋转、翻转、亮度调整和缩放等数据增强技术，自动处理视频数据。实验结果表明，在联邦学习环境下，我们的框架实现了89.9%的检测准确率，在各种部署场景下优于现有方法。结果展示了我们方法在处理现实世界数据变化方面的有效性，并突显了其在智能交通系统中部署的潜力，通过早期可靠的疲劳检测来提高道路安全。 显示英文摘要

摘要： Driver drowsiness is one of the main causes of road accidents and is recognized as a leading contributor to traffic-related fatalities. However, detecting drowsiness accurately remains a challenging task, especially in real-world settings where facial data from different individuals is decentralized and highly diverse. In this paper, we propose a novel framework for drowsiness detection that is designed to work effectively with heterogeneous and decentralized data. Our approach develops a new Spatial Self-Attention (SSA) mechanism integrated with a Long Short-Term Memory (LSTM) network to better extract key facial features and improve detection performance. To support federated learning, we employ a Gradient Similarity Comparison (GSC) that selects the most relevant trained models from different operators before aggregation. This improves the accuracy and robustness of the global model while preserving user privacy. We also develop a customized tool that automatically processes video data by extracting frames, detecting and cropping faces, and applying data augmentation techniques such as rotation, flipping, brightness adjustment, and zooming. Experimental results show that our framework achieves a detection accuracy of 89.9% in the federated learning settings, outperforming existing methods under various deployment scenarios. The results demonstrate the effectiveness of our approach in handling real-world data variability and highlight its potential for deployment in intelligent transportation systems to enhance road safety through early and reliable drowsiness detection.