# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 3 Materials and Methods
### CA-C3K2 Module  
In fatigue detection for ATCOs, the recognition of fine-grained biometric features in facial regions constitutes a critical factor in constructing deep learning detection models. Figure 6: Comparison of the original number of positive and negative class images in the dataset. Research [32] has identified several appearance characteristics across different facial regions during fatigue states, specifically manifested as eyelid puffiness, hyperpigmentation caused by dilated infraorbital venous vasculature, and drooping mouth corners due to decreased muscle tone in facial muscle groups. In actual ATC working environments, ATCOs' faces are often subjected to complex non-uniform illumination caused by mixed indoor and outdoor light sources, screen glare from equipment, and localized shadows. Such interference tends to cause degradation in feature extraction for the YOLO11 model based on the C3K2 module, leading to blurred features in shadowed areas of ATCOs' facial images and reduced local contrast. This significantly impairs the model's discriminative capability for key fine-grained fatigue-related biometric features, such as eyelid puffiness, lower-eyelid pigmentation, and lip-corner drooping. To address the aforementioned issues, this study proposes the CA-C3K2 module incorporating a channel attention mechanism. The CA-C3K2 module improves upon the C3K2 module used in YOLO11. The original structure of C3K2 is shown in Figure 7. When the parameter C3K is True, the module contains n C3K blocks for extracting key features, as illustrated in Figure 7a; when the parameter C3K is False, C3K2 uses n bottleneck modules instead, as shown in Figure 7b. As a key feature extraction component of YOLO11, the C3K2 module introduces a more flexible feature processing mechanism while inheriting the architecture of the C2f module. When feature map x is input to the module, it first undergoes channel adjustment via the CBS module. Equation (5) represents the mathematical expression of this module. \[F_{\text{conv}}=\sigma(\text{BN}(\text{Bottleneck}(X))) \tag{5}\]  
In the equation, \(\sigma\) denotes the Silu activation function, and BN represents the batch normalization function. After adjustment by the CBS module, the feature map undergoes channel splitting, forming two processing paths: one portion is directly preserved for final  
Figure 7: Comparison of module structures between C3K2 and CA-C3K2. Subfigure (**a**) is the structure diagram of the C3K2 module when the C3K parameter is True; Subfigure (**b**) is the structure diagram of the C3K2 module when the C3K parameter is False; Subfigure (**c**) is the structure diagram of the CA-C3K2 proposed in this study. The number of C3K modules in Subfigure (**a**) and the number of Bottleneck modules in Subfigure (**b**) are both determined by the parameter n.  
concatenation, while the other passes through n intermediate processing modules. The type of these intermediate modules is determined by the parameter C3K, and the number of modules is determined by the parameter n. The output features from all intermediate modules are concatenated along the channel dimension with the initially preserved features, achieving multi-level feature fusion. However, the Bottleneck/C3K modules within the processing branches of the C3K2 module employ standard convolution operations, lacking an explicit cross-channel attention mechanism. This results in insufficient modeling of inter-channel dependencies. Such a design limits the modeling capability of the C3K2 module regarding feature correlations across channel dimensions, thereby impacting the accuracy of fatigue recognition in complex lighting environments. To address the problems of fine-grained fatigue facial features and feature degradation under complex illumination interference in the ATCO fatigue detection task, CA-C3K2 modifies the C3K2 module architecture by employing two C3K modules for fatigue feature extraction while incorporating channel attention into each branch.