# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 4 Results
### Ablation Experiment  
The FF-YOLO model proposed in this paper is based on YOLO1n and introduces three different improvements. To verify the effectiveness of each improvement in the FF-YOLO model, ablation experiments of the three improved modules were conducted on the ATCO facial fatigue dataset. The experimental results are shown in Table 6, where \"\(\times\)\" indicates that a specific module is not used and \"\(\surd\)\" indicates that the module is used. In Table 6, YOLO-CA denotes YOLO1n with the CA-C3K2 module; YOLO-C refers to YOLO11n incorporating the CBAM in its head network; YOLO-M represents YOLO1n using the MPDIoU loss function; and FF-YOLO combines all three improvements. Table 6 provides the precision, recall, mAP@50, and mAP@50-95 values of each ablation model. It can be seen that after YOLO-CA replaced all C3K2 modules in YOLO11 with CA-C3K2, mAP@50 and mAP@50-95 increased by 10.4% and 11.3%, respectively, precision increased by 1.1%, and recall increased by 6.2%, at a 24% computational cost. This improvement primarily stems from the channel attention mechanism within the CA-C3K2 module, which dynamically recalibrates channel-wise feature responses. By emphasizing informative channels and suppressing less useful ones, the module enhances fine-grained fatigue feature extraction capability under complex illumination conditions. The model parameter increase is attributed to the replacement of all original C3K2 modules in YOLO11n with CA-C3K2 counterparts, where the latter exhibits marginally higher structural complexity while being extensively employed throughout the network architecture. After YOLO-C added the CBAM attention mechanism to each detection head of the head network, mAP@50 and mAP@50-95 increased by 11.9% and 8.5%, respectively, precision increased by 1.4%, and re \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CA-C3K2**} & \multirow{2}{*}{**CBAM**} & \multirow{2}{*}{**MPIoU**} & \multirow{2}{*}{**mAP@50 (\%)**} & \multirow{2}{*}{**mAP@50-95 (\%)**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{
\begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular} } \\  & & & & & & & & \\ \hline YOLO1n & \(\times\) & \(\times\) & \(\times\) & 80.5 & 63.1 & 83.2 & 67.9 & 2,582,347 & 6.3 \\ YOLO-CA & \(\surd\) & \(\times\) & \(\times\) & 90.9 & 74.4 & 84.3 & 74.1 & 3,220,129 & 7.6 \\ YOLO–C & \(\times\) & \(\surd\) & \(\times\) & 92.4 & 71.6 & **84.6** & 71.8 & 2,582,542 & 6.3 \\ YOLO–M & \(\times\) & \(\times\) & \(\surd\) & 82.7 & 63.7 & 82.7 & 68.3 & 2,582,347 & 6.3 \\ FF-YOLO (ours) & \(\surd\) & \(\surd\) & \(\surd\) & **94.2** & **74.7** & 83.8 & 73.8 & 3,220,324 & 7.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of ablation models. Figure 13: Controller fatigue detection results under different conditions. Subfigure (**a**) is under daylight conditions, Subfigure (**b**) is when ATCO wearing glasses, Subfigure (**c**) is under indoor lighting conditions at night, and Subfigure (**d**) is when ATCO’s head is tilted. call increased by 3.9%. This is attributed to the fact that CBAM can integrate the spatial and channel features of fatigue, improving the model's fatigue detection ability under occlusion and head deflection. After YOLO-M replaced the original CIoU loss function with the MPDIoU loss function, although the precision dropped by 0.5%, the recall rate increased by 0.4%, and the mAP@50 and mAP@50-95 increased by 2.2% and 0.6%, respectively, while the number of parameters and computational complexity remained unchanged. This shows that the MPDIoU loss function can effectively improve the detection accuracy of the model without affecting detection performance. By integrating all aforementioned enhancement mechanisms, the FF-YOLO model improved mAP@50, mAP@50-95, precision, and recall by 13.7%, 11.6%, 0.6% and 5.9%, respectively, at a 24% additional computational cost, compared with the original YOLO11n model.