基于改进YOLOv8n的环卫车辆驾驶员疲劳检测方法  

A Study on Fatigue Detection for Sanitation Vehicle Drivers Based on Improved YOLOv8n

作　　者：仝光[1] 赵博 随婷婷 刘书炘 Tong Guang;Zhao Bo;Sui Tingting;Liu Shuxin(Shanghai Dianji University,Shanghai 201306)

机构地区：[1]上海电机学院,上海201306

出　　处：《汽车技术》2025年第3期15-21,共7页Automobile Technology

基　　金：国家自然科学基金项目(62103256);福建省高校重点实验室开放课题基金项目(KLCCIIP202203)。

摘　　要：针对环卫车驾驶员的驾驶环境和驾驶安全,提出了一种基于改进YOLOv8n算法的驾驶员疲劳检测方法。使用FasterNet替换YOLOv8n目标检测算法的主干网络,并设计FasterNet-YOLO的轻量级网络模型;在主干网络和颈部中分别加入压缩和激励(SE)模块与卷积注意力机制(CBAM)模块,保留输入的重要特征信息;引入Zero-DCE++算法对摄像头输入的视频流进行亮度增强,处理因驾驶员面部亮度不足所致模型难以识别问题。试验结果表明:交并比为0.5时的平均类别检测精度(mAP@0.5)达到98%,平均每帧图片推理时间缩短至6.95 ms;该方法在不同光照情况下,均能够实时、准确地检测驾驶员疲劳状态。With regard to the driving environment and safety of sanitation vehicle drivers,this paper proposes a driver fatigue detection method based on an enhanced YOLOv8n algorithm.Specifically,FasterNet is employed to replace the backbone network of the YOLOv8 object detection algorithm,resulting in the design of a lightweight FasterNet-YOLO network model.To preserve critical feature information from the input feature map,Squeeze-and-Excitation(SE)modules are integrated into the backbone network,while Convolutional Block Attention Modules(CBAM)are added to the neck network.Additionally,the Zero-DCE++algorithm is introduced to enhance the brightness of video streams captured by cameras,addressing the issue of insufficient brightness in the driver’s face that hinders accurate detection.Experimental results demonstrate that the proposed method achieves an average precision of 98%(mAP@0.5)at an intersection over union ratio of 0.5,with an average inference time per frame reduced to 6.95 ms.This approach can effectively monitor the driver’s fatigue state in real-time under varying lighting conditions.

关 键 词：疲劳驾驶 目标检测 FasterNet-YOLO 注意力机制 低光增强 

分 类 号：U492.84[交通运输工程—交通运输规划与管理]