# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 5 Discussion  
To address the issues of poor real-time performance with subjective methods, interference with ATCO operations from intrusive methods, and insufficient anti-interference capability of machine learning methods in air traffic controller fatigue detection, this study proposes FF-YOLO, an improved deep learning approach based on facial image features using YOLO1n. First, a facial image dataset of ATCOs under radar control scenarios was constructed, containing facial data from 10 ATCOs. Fatigue states were annotated through subjective fatigue scale ratings and PERCLOS metrics, with category balancing applied to ultimately obtain a dataset of 25,154 fatigue facial images containing an equal number of positive and negative class samples. To enhance detection capability for ATCO facial fatigue features, a CA-C3K2 module was proposed to replace the original C3K2 module. Additionally, a CBAM was incorporated into the network head section to improve detection performance for occluded facial fatigue features. Finally, the original CIoU loss function was replaced with MPDIoU to accelerate model convergence and enhance detection capability for facial images of varying sizes. Experimental results show that the mAP@50, mAP@50-95, precision, and recall of the FF-YOLO model reach 94.2%, 74.7%, 83.8%, and 73.8%, respectively, which are 13.7%, 11.6%, 0.6%, and 5.9% higher than those of YOLO1n. The model can correctly detect ATCO fatigue under conditions of complex light interference, facial occlusion, and facial deflection. The current study has limitations in the current dataset's scale and diversity, as it comprises fatigue states from only 10 controllers. To strengthen the model's generalizability, future research will expand the dataset to include larger and more diverse ATCO populations across multiple operational environments. It will further validate the model's generalizability and robustness in real-world applications and support future work on model improvement.  
Figure 15: Comparison of training phase performance metrics between FF-YOLO and YOLO1n Models. Subfigure (**a**) illustrates the variation of Precision metric of the two models with the increase of training epochs. Subfigure (**b**) depicts the change of Recall metric. Subfigure (**c**) depicts the change of mAP@50 metric. Subfigure (**d**) depicts the change of Loss value.  
**Author Contributions:** Conceptualization, S.T. and W.P.; methodology, S.T.; software, S.T. and L.D.; validation, S.T. and Q.Z.; formal analysis, S.T. and L.D.; investigation, S.T. and. Q.Z.; resources, W.P.; data curation, S.T., L.D., and Y.Z.; writing--original draft preparation, S.T.; writing--review and editing, L.D. and Q.Z.; visualization, S.T. and Y.Z.; supervision, W.P.; project administration, S.T.; funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (U2333209), the Key Laboratory of Flight Techniques and Flight Safety CAAC (F2024 KF11C), the Fundamental Research Funds for the Central Universities (24 CAFUC01002), and the Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan Province (MZ2024 JB01).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Civil Aviation Flight University of China.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.  
**Data Availability Statement:** The data collected and annotated in this study are not publicly available due to confidentiality agreements and privacy protection concerns for the participants. For requests to access the dataset, please contact the corresponding author at [PERSON]RESS].  
**Conflicts of Interest:** The authors declare no conflicts of interest.