YOLO11-CR: a Lightweight Convolution-and-Attention Framework for Accurate Fatigue Driving Detection

TL;DR

YOLO11-CR is a lightweight, real-time detection model combining convolution and attention mechanisms to improve fatigue driving detection accuracy, especially for small and occluded objects.

Contribution

The paper introduces YOLO11-CR, a novel lightweight detection framework with CAFM and RCM modules for enhanced feature fusion and spatial localization in fatigue detection.

Findings

Achieves 87.17% precision and 88.09% mAP@50 on DSM dataset.

Outperforms baseline models significantly in accuracy.

Validated effectiveness of CAFM and RCM modules through ablation studies.

Abstract

Driver fatigue detection is of paramount importance for intelligent transportation systems due to its critical role in mitigating road traffic accidents. While physiological and vehicle dynamics-based methods offer accuracy, they are often intrusive, hardware-dependent, and lack robustness in real-world environments. Vision-based techniques provide a non-intrusive and scalable alternative, but still face challenges such as poor detection of small or occluded objects and limited multi-scale feature modeling. To address these issues, this paper proposes YOLO11-CR, a lightweight and efficient object detection model tailored for real-time fatigue detection. YOLO11-CR introduces two key modules: the Convolution-and-Attention Fusion Module (CAFM), which integrates local CNN features with global Transformer-based context to enhance feature expressiveness; and the Rectangular Calibration Module (RCM), which captures horizontal and vertical contextual information to improve spatial localization, particularly for profile faces and small objects like mobile phones. Experiments on the DSM dataset demonstrated that YOLO11-CR achieves a precision of 87.17%, recall of 83.86%, mAP@50 of 88.09%, and mAP@50-95 of 55.93%, outperforming baseline models significantly. Ablation studies further validate the effectiveness of the CAFM and RCM modules in improving both sensitivity and localization accuracy. These results demonstrate that YOLO11-CR offers a practical and high-performing solution for in-vehicle fatigue monitoring, with strong potential for real-world deployment and future enhancements involving temporal modeling, multi-modal data integration, and embedded optimization.