# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 3 Materials and Methods
### FF-YOLO Network
#### 3.3.2 Head Network with Spatial-Channel Attention Mechanism  
In the ATCO fatigue detection task, key fatigue features in facial images can lead to reduced detection accuracy due to interference from factors such as glasses occlusion and head rotation. Although the traditional YOLO11 model possesses object detection capabilities, it struggles to effectively differentiate differential responses in channel dimensions for fatigue-related features like darkened lower eyelid pigmentation and drooping mouth corners under interference conditions such as occlusion and rotation. Additionally, it lacks dynamic focus capability on target spatial regions, causing subtle facial fatigue features to be overlooked. To address this, this study embeds the convolutional block attention module (CBAM) [35] at the neck level of the YOLO11 network. The overall architecture of CBAM adopts a cascaded dual-attention mechanism, as shown in Figure 10a. Its processing flow follows a \"channel-first, spatial-second\" cascading paradigm: the input feature tensor first passes through the channel attention module (CAM) to generate a channel-wise attention weight matrix. This adjusts the channel attention weights of the original features via Hadamard product operations, achieving dynamic scaling of the feature map in the channel dimension and strengthening activation responses for task-relevant channels. Subsequently, the channel-optimized feature tensor is fed into the spatial attention module (SAM), which performs dynamic weight modulation on the same positions across multiple channels of the feature map using a spatial dimension weight map, suppressing background noise and enhancing semantic responses in target regions. The synergistic effect of these two submodules forms a staged feature optimization mechanism, where channel attention filters key feature channels through global statistical modeling, while spatial attention focuses on semantically salient regions based on local contextual relationships. This design aligns with the cognitive logic of \"feature filtering-spatial localization\". The entire CBAM achieves feature selection in channel and spatial dimensions through two independent yet complementary attention operations while preserving the topological structure of the original feature map. This dual-attention collaborative mechanism enhances the model's feature representation capability with only a minor increase in parameters. The architecture of the CBAM is shown in Figure 10b. The module first performs spatial compression operations on the input feature tensor \(X\in\mathbb{R}^{H\times W\times C}\), using global average pooling and global maximum pooling. The feature representations formed by the two concatenate constitute the channel descriptor. The two-way feature is then fed into a multi-layer perceptron (MLP) with shared weights, which implements dimensionality reduction-nonlinear transformation-dimensionality increase operations through a bottleneck structure. After being processed by the two-way feedforward network, the feature vector achieves information aggregation through element-by-element summation and generates channel attention weights through the Sigmoid activation function. The mathematical expression of the channel attention mechanism weight \(M_{C}\in\mathbb{R}^{1\times 1\times C}\) in the CBAM is shown in Formula 11. \[M_{C}(X)=\sigma(MLP(AvgPool(X))+MLP(MaxPool(X))) \tag{11}\]The spatial attention module (SAM) in CBAM dynamically adjusts feature response weights at different spatial positions by modeling spatial dimension relationships within the feature map, thereby guiding the model to focus on salient regions. Its core processing flow is shown in Figure 10c, with the mathematical expression for the spatial attention weight \(M_{S}\in\mathbb{R}^{H\times W\times 1}\) given in Equation (12).