# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
###### Abstract  
Real-time detection of fatigue states in air traffic controllers (ATCOs) is crucial for ensuring air traffic safety. Existing methods exhibit limitations such as poor real-time performance, intrusiveness, and susceptibility to lighting and occlusion. This paper proposes FF-YOLO, an improved YOLO11-based deep learning algorithm, to detect ATCO fatigue states through facial feature analysis. A custom dataset comprising 25,154 facial images collected from 10 ATCOs was constructed for model training and validation. The FF-YOLO model introduces the CA-C3K2 module for fine-grained feature extraction under complex lighting, incorporates a spatial-channel attention mechanism for improved detection accuracy during occlusion, and MPDIoU loss for enhanced accuracy on multi-scale facial images with accelerated convergence. Experimental results show FF-YOLO achieves 94.2% mAP@50, 74.7% mAP@50-95, 83.8% precision, and 73.8% recall, with gains of +13.7%, +11.6%, +0.6%, and +5.9% over YOLO11n, respectively, thereby enabling real-time and accurate detection of ATCO fatigue states. Future work will expand datasets with larger and more diverse ATCO populations to enhance generalizability. air traffic controllers; fatigue detection; YOLO11; deep learning; facial features 2025  
11 June 20251 July  
they cannot achieve real-time monitoring of fatigue states, and second, they lack precise judgment tailored to individual differences. Therefore, developing a real-time monitoring method for detecting ATCO fatigue states to understand their work conditions in real time and form a \"pre-event warning\"-dynamic intervention\" management model is of great significance for enhancing the proactivity and precision of aviation safety management. Fatigue detection research can be divided into subjective evaluation methods and objective evaluation methods. Subjective fatigue evaluation methods use standardized questionnaires or scales where ATCOs self-assess based on their own perceptions [8]. Although this approach can accurately assess subjective fatigue states, it cannot achieve real-time detection. Therefore, objective evaluation methods based on physiological indicators are used for ATCO fatigue detection. These methods utilize electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals as criteria for fatigue assessment [9]. However, such intrusive methods require subjects to wear devices, which can interfere with ATCOs' normal work [10]. With advancements in computing power and algorithmic innovations, non-intrusive fatigue detection technologies based on machine learning have become a research hotspot in the field of fatigue detection. Several scholars have proposed various machine learning methods for fatigue detection [11]. These methods typically involve extracting facial key points, calculating mouth and eyelid closure degrees, and then using machine learning algorithms to build models for facial fatigue detection. However, machine learning methods are susceptible to various interferences in ATCO work environments. For example, ATCOs need to verbally broadcast control instructions during their work, which can severely affect the accuracy of fatigue detection methods based on mouth opening and closing degrees. Additionally, changes in eyelid closure degrees under different lighting conditions and occlusions, such as wearing glasses, can reduce the accuracy of detection based on the percentage of eyelid closure time, making traditional machine learning methods less practical due to the influence of work characteristics and environmental complexities. To address the above issues, this paper focuses on ATCOs and proposes a deep learning method for fatigue detection using facial visual features based on an improved YOLO1, named FF-YOLO. The contributions of this paper are as follows:  
1. A dataset of facial images of ATCOs under radar control scenarios was established, including facial image data of 10 ATCOs during work.