Arava Mohan, Sundaram Divya Meena
SCOPE, VIT-AP University, Amaravathi, AP, India.
PeerJ Comput Sci. 2024 Dec 5;10:e2447. doi: 10.7717/peerj-cs.2447. eCollection 2024.
Several factors cause vehicle accidents during driving, such as driver negligence, drowsiness, and fatigue. These accidents can be prevented if drivers receive timely warnings. Additionally, recent advancements in computer vision and artificial intelligence (AI) have enabled the monitoring of drivers and the ability to alert them when they are not focused on driving. AI techniques can analyse key facial features, such as eye closure, yawning, and head movements, to assess the driver's level of sleepiness. In response to the growing concerns surrounding drowsy driving and its potential safety hazards, this study presents a comprehensive approach for detecting a driver's attention state using an enhanced version of the You Only Look Once (YOLOv5) algorithm. By leveraging critical facial landmarks and calculating the eye and mouth aspect ratios, the method effectively identifies signs of fatigue by establishing threshold values indicative of closed eyes and yawning. This work introduces an advanced YOLOv5 model integrated with Swin Transformer modules in the feature fusion network and refined backbone network feature extraction to detect driver drowsiness. Additionally, a real-time fatigued-driving detection model, built on an improved YOLOv5s architecture and incorporating Attention Mesh 3D key points, demonstrates superior effectiveness over conventional models. The proposed method achieves a notable 2.4% enhancement in mean average precision (mAP) compared to the baseline model through extensive experimentation on benchmark datasets. By combining YOLOv5 with facial 3D landmarks, the system benefits from the complementary strengths of both techniques, leading to more accurate and robust detection of fatigue-related cues and ultimately mitigating accidents caused by drowsy driving.
驾驶过程中，有几个因素会导致车辆事故，比如驾驶员疏忽、困倦和疲劳。如果驾驶员能及时收到警告，这些事故是可以预防的。此外，计算机视觉和人工智能（AI）的最新进展使得对驾驶员进行监测成为可能，并能在他们注意力不集中于驾驶时发出警报。人工智能技术可以分析关键面部特征，如闭眼、打哈欠和头部动作，以评估驾驶员的困倦程度。针对人们对疲劳驾驶及其潜在安全隐患日益增长的担忧，本研究提出了一种综合方法，使用改进版的You Only Look Once（YOLOv5）算法来检测驾驶员的注意力状态。通过利用关键面部标志点并计算眼睛和嘴巴的长宽比，该方法通过建立指示闭眼和打哈欠的阈值来有效识别疲劳迹象。这项工作引入了一种先进的YOLOv5模型，该模型在特征融合网络中集成了Swin Transformer模块，并对骨干网络特征提取进行了优化，以检测驾驶员困倦状态。此外，基于改进的YOLOv5s架构并结合注意力网格3D关键点构建的实时疲劳驾驶检测模型，相较于传统模型具有更高的有效性。通过在基准数据集上进行广泛实验，与基线模型相比，该方法在平均精度均值（mAP）上显著提高了2.4%。通过将YOLOv5与面部3D标志点相结合，该系统受益于两种技术的互补优势，从而更准确、更稳健地检测与疲劳相关的线索，并最终减少因疲劳驾驶导致的事故。
