基于轻量级主干的YOLOv5驾驶员疲劳检测算法

Driver Fatigue Detection Algorithm Based on Lightweight YOLOv5

针对目前基于深度学习的驾驶员疲劳检测算法存在着参数量和计算成本较大,难以在算力较低的设备上得到有效应用这一问题,提出一种基于轻量级主干的YOLOv5驾驶员疲劳检测算法,通过检测闭眼、张嘴、低头这3种标签的时间占比来进行疲劳判断。算法使用EfficientViT网络作为模型的主干网络,降低了整个模型的参数量以及计算成本,在模型的颈部网络部分加入上下文变换器模块并将归一化沃瑟斯坦距离作为新的损失函数以此来提高模型的准确度,减小轻量级主干所带来的损失。实验结果表明:改进后的算法准确率达到97.9%,与YOLOv5、YOLOv7、YOLOv8相比,其参数量分别降低了3.4、17.7和5.4倍,计算量分别降低了4.5、29.5和8.2倍,在CPU上的单幅图片推理速度加快至76.4 ms,能够有效地完成实时检测任务。

Aiming at the problem that the current driver fatigue detection algorithm based on deep learning requires a large number of parameters and calculation costs,and it is difficult to be effectively applied on low-computing devices,a YOLOv5 driver algorithm based on a lightweight backbone was proposed.Fatigue was judged by detecting the proportion of time that the three labels of closing eyes,opening mouth,and lowering head occupy.The EfficientViT network was utilized as the backbone network of the model,resulting in a reduction in both the model's parameter count and computational costs.Within the bottleneck network section of the model,a contextual transformer module was integrated,and the normalized Wasserstein distance was adopted as the new loss function.This was done to enhance the model's accuracy and alleviate any potential losses caused by the lightweight backbone.The experimental results show that the improved algorithm has an accuracy rate of 97.9%.Compared with YOLOv5,YOLOv7,and YOLOv8,its parameters are reduced by 3.4,17.7,and 5.4 times,and the amount of calculation is reduced by 4.5,29.5,and 8.2 times.The inference speed of a single image on the CPU is accelerated to 76.4 ms,and it can effectively complete the real-time detection task.