Ran Xiansheng, He Shuai, Li Rui
School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Sensors (Basel). 2023 Oct 6;23(19):8267. doi: 10.3390/s23198267.
In response to the problem of high computational and parameter requirements of fatigued-driving detection models, as well as weak facial-feature keypoint extraction capability, this paper proposes a lightweight and real-time fatigued-driving detection model based on an improved YOLOv5s and Attention Mesh 3D keypoint extraction method. The main strategies are as follows: (1) Using Shufflenetv2_BD to reconstruct the Backbone network to reduce parameter complexity and computational load. (2) Introducing and improving the fusion method of the Cross-scale Aggregation Module (CAM) between the Backbone and Neck networks to reduce information loss in shallow features of closed-eyes and closed-mouth categories. (3) Building a lightweight Context Information Fusion Module by combining the Efficient Multi-Scale Module (EAM) and Depthwise Over-Parameterized Convolution (DoConv) to enhance the Neck network's ability to extract facial features. (4) Redefining the loss function using Wise-IoU (WIoU) to accelerate model convergence. Finally, the fatigued-driving detection model is constructed by combining the classification detection results with the thresholds of continuous closed-eye frames, continuous yawning frames, and PERCLOS (Percentage of Eyelid Closure over the Pupil over Time) of eyes and mouth. Under the premise that the number of parameters and the size of the baseline model are reduced by 58% and 56.3%, respectively, and the floating point computation is only 5.9 GFLOPs, the average accuracy of the baseline model is increased by 1%, and the Fatigued-recognition rate is 96.3%, which proves that the proposed algorithm can achieve accurate and stable real-time detection while lightweight. It provides strong support for the lightweight deployment of vehicle terminals.
针对疲劳驾驶检测模型计算量和参数要求高以及面部特征关键点提取能力弱的问题，本文提出了一种基于改进的YOLOv5s和注意力网格3D关键点提取方法的轻量级实时疲劳驾驶检测模型。主要策略如下：（1）使用Shufflenetv2_BD重构骨干网络，以降低参数复杂度和计算量。（2）引入并改进骨干网络与颈部网络之间的跨尺度聚合模块（CAM）的融合方法，以减少闭眼和闭嘴类别浅层特征中的信息损失。（3）通过结合高效多尺度模块（EAM）和深度可分离过参数卷积（DoConv）构建轻量级上下文信息融合模块，以增强颈部网络提取面部特征的能力。（4）使用Wise-IoU（WIoU）重新定义损失函数，以加速模型收敛。最后，将分类检测结果与眼睛和嘴巴的连续闭眼帧数、连续打哈欠帧数以及PERCLOS（瞳孔遮挡率随时间变化）阈值相结合，构建疲劳驾驶检测模型。在参数数量和基线模型大小分别减少58%和56.3%，且浮点运算仅为5.9 GFLOPs的前提下，基线模型的平均准确率提高了1%，疲劳识别率为96.3%，证明了所提算法在轻量级的同时能够实现准确稳定的实时检测。它为车辆终端的轻量级部署提供了有力支持。
