# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 3 Materials and Methods
### Dataset Construction
#### 3.1.2 Dataset Labeling
According to the PERCLOS algorithm, when the eyes are closed for a period of time, it can be judged that the individual may be in a state of fatigue. The specific method for calculating the PERCLOS value is to use the EAR value to determine the degree of eye closure at different time points. This study uses the P80 standard [27] to assess fatigue by the percentage of time when the eye closure area exceeds 80%, that is, the time Tclose when the EAR < 0.2, in the total observation time T. The calculation formula for the PERCLOS value can be expressed as Equation (3) [30]:  
\[Perclos=\frac{T_{close}}{T}\times 100\% \tag{3}\]  
In practical applications, since all collected videos are segmented into facial images at a rate of 1 frame per second, PERCLOS can be expressed as the ratio of the number of facial images with eye closure within one minute to the total number of images. Thus, PERCLOS can be represented as shown in Equation (4). Here, Fclose denotes the number of facial images within one minute where the EAR is less than 0.2, and F represents the total number of facial images within one minute, with F = 60 in this experiment. \[Perclos=\frac{F_{close}}{F}\times 100\% \tag{4}\]  
Figure 4: Selected eye key points. Subfigure (**a**) shows the positioning of the key points of the eyes when not wearing glasses; Subfigure (**b**) shows the positioning of the key points of the eyes when wearing glasses and looking directly at the screen. It can be found that although there is a deviation in this case, the impact is minimal. Figure 5: Illustration of the EAR calculation method based on six key points. The PERCLOS threshold of the P80 standard is set at 30% under high-concentration tasks; that is, when the PERCLOS value is detected to be greater than 30%, it is considered that the controller is objectively in a state of fatigue. The fatigue labeling, combining subjective and objective measures, adopted the NASA-TLX subjective scale and the PERCLOS method. The fatigue classification standard used in this study is defined as follows: when a subject's PERCLOS value exceeds 30% and the NASA-TLX composite evaluation score is \(\geq\)7, the facial image is classified as a fatigue state and labeled as \"Drowsy\"; otherwise, it is classified as a non-fatigue state and labeled as \"Non-Drowsy\". This classification standard is adopted because a PERCLOS value exceeding 30% indicates that, from a physiological perspective, the ATCO's eye closure time is excessively long, suggesting physiological signs of fatigue; meanwhile, a NASA-TLX subjective scale score between 7 and 10 reflects significant workload and psychological fatigue perceived by the ATCO. The combination of subjective and objective standards effectively reduces misjudgments that may arise from a single metric, thereby improving the accuracy and reliability of fatigue detection. For example, in practical operations, even if the PERCLOS value slightly exceeds 30%, a low subjective scale score may indicate only momentary distraction or inattention rather than true fatigue; conversely, if the subjective scale score is high but the PERCLOS value does not reach the threshold, it may reflect temporary discomfort due to psychological factors or other reasons without affecting actual performance. Therefore, the combined subjective and objective evaluation method can more accurately identify ATCOs who are truly in a fatigued state. After labeling, the dataset contains a total of 12,577 \"Drowsy\" images and 41,179 \"Non-Drowsy\" images of various ATCOs, as shown in Figure 6. It is evident that the number of fatigue state images is significantly lower than that of non-fatigue state images, as the experiment induced fatigue states through control tasks. However, an imbalanced dataset can lead to a model bias toward the more numerous non-fatigue category, making it difficult for the model to effectively learn representative features of the fatigue category [31].