# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 4 Results
### Ablation Experiment
Ablation experiments demonstrate that each enhancement contributes positively to model performance, validating the effectiveness of the FF-YOLO architecture. The FF-YOLO elevates ATCO fatigue detection accuracy in complex environments as a practical non-intrusive monitoring solution. Figure 14 shows the P-R curves of each ablation model. Each figure contains the positive and negative samples and the overall P-R curve of the corresponding model. The positive and negative sample curves of the YOLO11n model are clearly separated, and the right half is obviously concave, indicating that the recognition ability of fatigue samples is poor and the model feature discrimination is insufficient; the positive and negative sample curves of the YOLO-CA model are closely adjacent and close to the upper right protrusion, indicating that the model has greatly improved its feature recognition ability after using the CA-C3K2 module, and can capture the distinguishing features of two types of samples at the same time; the overall curve of the YOLO-C model is closer to the upper right than YOLO-CA, but the positive and negative sample curves are separated to a certain extent, indicating that the overall performance of the model is excellent but there is a category bias. The positive and negative sample curves of the YOLO-M model are closer than those of YOLO11n, indicating that the optimization of the loss function helps the model learn the features of different types of samples. The FF-YOLO model curve is the best among them. It is closer to the upper right corner than YOLO-CA, and the distance between the positive and negative sample curves is closer than that of the YOLO-C model, and the AP is also the largest. This shows that FF-YOLO can combine the advantages of various improvements, has strong overall performance, and is more balanced in identifying different categories, which is a significant improvement over YOLO11n. Figure 15 compares the performance of YOLO11n and FF-YOLO in the precision curve, recall curve, mAP@50 curve, and loss curve. It can be clearly seen that the improved model has significant improvements in the three key indicators of recall rate, mAP@50, and training loss. Although FF-YOLO only improves 0.6% over YOLO11n in terms of precision, it has achieved a 5.9% improvement in recall, which shows that FF-YOLO has enhanced the feature extraction capability through CA-C3K2, improved the model's recognition ability for blurred samples under light interference, and reduced the missed detection rate. At the same time, the mAP@50 value increased by 13.7%, which also shows that the introduction of the spatial-channel attention mechanism in the neck area can enhance the model's generalization ability for occluded samples, and can more accurately detect fatigue status when glasses are occluded. It can be seen from Figure 15 that the LOSS value of the FF-YOLO model decreases significantly faster than that of YOLO11n. This is because FF-YOLO uses the MPDIoU loss function, which makes the model converge faster. These improvements fully demonstrate that the FF-YOLO model has better performance and stronger detection capabilities in the task of controller fatigue detection. The ablation experiment results show that each improvement has a positive impact on the performance of different aspects of the model, verifying the effectiveness of the FF-YOLO model design. Figure 14: P-R curves of ablation models.