Mfdd: Multi-scale attention fatigue and distracted driving detector based on facial features

Abstract

With the rapid expansion of the automotive industry and the continuous growth of vehicle fleets, traffic safety has become a critical global social issue. Developing detection and alert systems for fatigue and distracted driving is essential for enhancing traffic safety. Factors, such as variations in the driver’s facial details, lighting conditions, and camera pixel quality, significantly affect the accuracy of fatigue and distracted driving detection, often resulting in the low effectiveness of existing methods. This study introduces a new network designed to detect fatigue and distracted driving amidst the complex backgrounds typical within vehicles. To extract driver and facial information as well as gradient details more efficiently, we introduce the Multihead Difference Kernel Convolution Module (MDKC) and Multiscale Large Convolutional Fusion Module (MLCF) in baseline. This incorporates a blend of Multihead Mixed Convolution and Large and Small Convolutional Kernels to amplify the spatial intricacies of the backbone. To extract gradient details from different illumination and noise feature maps, we enhance the network’s neck by introducing the Adaptive Convolutional Attention Module (ACAM) in NECK, optimizing feature retention. Extensive comparative experiments validate the efficacy of our network, showcasing superior performance not only on the Fatigue and Distracted Driving Dataset but also competitive results on the public COCO dataset. Source code is available at https://github.com/SCNU-RISLAB/MFDD.