# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 2 Related Works
Objective non-intrusive methods typically detect fatigue based on facial features. Conventional approaches often employed machine learning techniques to assess fatigue by measuring the percentage of eyelid closure time (PERCLOS) and yawning frequency based on mouth closure degree [18]. [PERSON] et al. [19] calculated the mouth aspect ratio (MAR) using oral keypoints and the eye aspect ratio (EAR) using ocular keypoints, subsequently determining fatigue based on the PERCLOS method. [PERSON] et al. [20] proposed a daily facial fatigue detection framework based on a non-local 3D attention network, which extracts spatio-temporal features using 3D-ResNet combined with a non-local attention mechanism for fatigue state classification. This approach achieved an average accuracy of 90.8% in fatigue detection tasks and a validation accuracy of 72.5% in binary classification scenarios. [PERSON] et al. [21] introduced a driver fatigue recognition framework based on intelligent facial expression analysis. It extracts multi-scale features from facial images through techniques including discrete wavelet transform, discrete cosine transform, and entropy analysis, and utilizes support vector machines for classification. This method attained an average expression recognition accuracy of 91.1%, demonstrating high robustness and accuracy under varying image resolutions, noise, and occlusion conditions. In recent years, significant advancements in hardware have substantially enhanced computer data processing capabilities, leading to the rapid development of deep learning-based object detection technologies. Deep learning-based non-intrusive methods overcome the limitation of manual feature extraction inherent in machine learning. They fully leverage the relationships within data and the learning capacity of the models themselves to automatically select features of the recognition targets, thereby improving detection efficiency and accuracy. The YOLO series models, as commonly used object detection algorithms, are widely applied in personnel fatigue detection due to their high detection accuracy, low parameter count, and fast detection speed. For instance, [PERSON] et al. [22] proposed ES-YOLO, an improved driver fatigue detection algorithm based on YOLOv7. To address issues of low fatigue accuracy and susceptibility to light interference, they integrated the CBAM module to enhance attention on crucial spatial locations within images and replaced the loss function with Focal-EIOU Loss to increase focus on hard samples. This network structure effectively ensured detection accuracy for driver fatigue states, achieving a 98.8% mAP on their self-built dataset. However, the model is not optimized for fatigue detection under occlusion. [PERSON] et al. [23] proposed Yolom-Net, a lightweight fatigue detection network based on YOLOv8. They replaced the original backbone network with MobilenetV3 to reduce computational complexity. Additionally, to focus on important image regions, they employed channel prior convolutional attention (CPCA) to enhance the network's feature extraction capability. This approach rapidly detected driver states while maintaining high accuracy, achieving 95.75% mAP and 47 fps on their self-built dataset. [PERSON] et al. [24] addressed the problem of inaccurate localization of driver facial fatigue features in real driving environments. They integrated a Gather mechanism from Gold-Yolo into YOLOv7 and reduced feature loss in the original network by adding a PSA module and replacing convolutions. Furthermore, their model incorporated an additional detection layer to enhance the extraction of eye and mouth features. The model has an accuracy of up to 98% on facial datasets, significantly enhancing the model's ability to locate fatigue facial features. However, the model relies on mouth features to judge fatigue, which is not practical in ATCO fatigue detection.