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.