| 殷旭鹏,赵兴强,王雄飞,阮琪,张菀.基于改进YOLOv10的驾驶疲劳检测算法[J].电子测量与仪器学报,2025,39(10):41-51 |
| 基于改进YOLOv10的驾驶疲劳检测算法 |
| Improved YOLOv10 algorithm for driver fatigue detection |
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| DOI： |
| 中文关键词: 小目标检测 驾驶疲劳检测 YOLOv10 注意力机制 损失函数 |
| 英文关键词:dim targets detection driver fatigue detection YOLOv10 attention mechanism loss function |
| 基金项目:国家自然科学基金面上项目（52077105）、江苏省自然科学基金面上项目（BK20211285）资助 |
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| Author | Institution |
| Yin Xupeng | 1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2.Jiangsu Province Key Laboratory of Big Data Analysis Technology and Intelligent Systems for Universities,
Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Zhao Xingqiang | 1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2.Jiangsu Province Key Laboratory of Big Data Analysis Technology and Intelligent Systems for Universities,
Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Wang Xiongfei | School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Ruan Qi | School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Zhang Wan | 1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2.Jiangsu Province Key Laboratory of Big Data Analysis Technology and Intelligent Systems for Universities,
Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| 中文摘要: |
| 疲劳驾驶的检测对于确保交通安全极为重要，实时监测并识别驾驶员的疲劳程度，配合预警机制，可有效降低因疲劳驾驶导致的事故风险。针对目前疲劳驾驶检测过程中驾驶员表情特征目标小、背景环境复杂的问题，提出了一种基于YOLOv10改进的疲劳驾驶检测模型—YOLOv10-GMF。该模型引入全局分组坐标注意力模块(global grouped coordinate attention, GGCA)，通过分组后的全局信息与局部特征处理，生成加权注意力图，实现信息压缩与特征表达，提升模型对疲劳状态下微小神态特征的捕捉能力。同时添加多尺度空洞融合模块(multi-dimension fusion attention, MDFA)，利用多尺度空洞卷积，并行融合空间和通道注意力机制，有效加强模型在复杂驾驶环境中对图像特征的识别能力。此外，为进一步优化训练过程，还设计了反馈信息驱动损失函数(feedback-driven loss, FDL)，有效加速模型的收敛过程，提高模型的检测效率。经过对比与消融实验，改进后的YOLOv10-GMF模型的检测平均精度均值（mAP）可达到98.1%，较YOLOv10提升了14.5%，且检测速度为64.3 fps。通过实际车载嵌入式部署测试，整个疲劳检测过程总耗时19.0 ms，完全满足驾驶过程中对疲劳状态进行实时监测的需求。 |
| 英文摘要: |
| Fatigue driving detection is critical for traffic safety. Real-time monitoring and accurate identification of a driver’s fatigue level, coupled with an early warning system, can significantly reduce the risk of accidents caused by fatigue. Addressing the challenges of small micro-expression targets and complex background environments in current driving fatigue detection, this paper proposes an improved driving fatigue detection model—YOLOv10-GMF. The model incorporates an enhanced global grouped coordinate attention (GGCA) module, which improves feature representation by weighting feature maps with global information and generating attention maps, thereby enhancing the model’s ability to capture micro-expression features under fatigue conditions. Additionally, a multi-dimension fusion attention (MDFA) module is integrated, which combines multi-scale dilated convolutions with spatial and channel attention mechanisms in parallel to strengthen the model’s recognition ability for image features in complex driving environments. To further optimize the training process, a feedback-driven loss function (FDL) is introduced, effectively accelerating model convergence and improving prediction accuracy. Ablation experiments demonstrate that the YOLOv10-GMF model achieves a detection accuracy of 98.1%, a 14.5% improvement over YOLOv10, with a detection speed of 64.3 fps. Through real vehicle embedded deployment tests, the average fatigue detection process takes 19.0 ms, and the model fully meets the real-time monitoring needs for fatigue driving. |
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