CHDS-YOLO: A Fatigue Driving Detection Algorithm in Low-light Environments

Aiming at the problem of low detection accuracy for driver fatigue in low-light environments, the CHDS-YOLO low-light fatigued driving detection algorithm based on the improved YOLOv9 is proposed. Firstly, lightweight improvements are made by replacing some conventional convolutions in the backbone network with Depthwise Separable Convolution (DSConv) to reduce the computational complexity of the model. In order to maintain performance during the lightweighting process, deformable convolutions are introduced to improve the RepNCSPELAN4 module, and the Reparameterized Deformable Convolutional Network (Rep_DCN) module is designed to enhance the feature extraction ability of the backbone network, so as to better handle detection targets with shape variations. Secondly, the Cross Hybrid Attention Fusion (CHAF) module is proposed. Through the cross structure, it can fuse the high-level and low-level features of images, highlight image details and alleviate the low-light problem. Finally, a visual channel attention mechanism is introduced into the detection neck. By combining global information with local information, it can capture the information relationships among different channels and selectively strengthen useful feature information. Experiments show that on the dataset of real scenes, the CHDS-YOLO algorithm proposed in this paper improves the mAP@0.5 by 1.6% and the mAP@0.5:0.95 by 1.5% compared with the original YOLOv9n algorithm. Meanwhile, it increases the FPS by 5.8% and reduces the number of model parameters by 13.3%. The accuracy of detecting driving fatigue in low-light environments and the lightweight design make it highly feasible in practical deployment.