## 4 SFA-YOLO Network
### SFA-YOLO Network Architecture  
The core of the SFA-YOLO network follows the one-stage detection approach of the YOLO series. As shown in Figure 3, this network structure follows the design principles of the YOLO series and applies the SFA mechanism. The model balances inference speed and precision and improves the detection of small targets like helmets and partially occluded targets like safety vests. The Backbone is responsible for extracting features from the input image. It consists of convolutional layers and specialized modules such as the SFA block. The main modules are as follows:  
1. The SFA block serves as the core component within the network and offers clear advantages. It balances lightweight design, computational efficiency, and memory usage. The SFA block strengthens the model's feature selection ability and suits deep learning tasks with high real-time requirements. The model adjusts channel weights dynamically to focus on key regions and suppress irrelevant information. This improves detection precision and robustness. It runs efficiently on devices with limited computational resources and adapts well to complex environments and diverse tasks. The structure of the SFA module is shown in Figure 4.
2. SPPF (Spatial Pyramid Pooling Fusion): SPPF strengthens the model's multi-scale feature extraction ability by incorporating spatial pyramid pooling. It performs pooling operations at different scales, enabling the model to capture hierarchical and multi-size features [19]. This fusion enhances the model's adaptability to object size and location variations while improving recognition in complex scenes [20; 21; 22; 23; 24]. Additionally, SPPF reduces feature map dimensions while preserving essential spatial information, improving computational efficiency and memory usage, which is especially useful for real-time object detection. Figure 3: Structure of the SFA-YOLO. 3. C2 PSA (Cross-Stage Partial with Self-Attention) enhances feature extraction with a combination of the CSP (Cross Stage Partial) structure and the PSA (Pyramid Squeeze Attention) mechanism. It improves multi-scale feature representation and strengthens both channel and spatial attention. This design strengthens the effectiveness of feature extraction and information fusion. C2 PSA uses channel attention to select important feature channels and suppress irrelevant information. This process improves the quality of feature representation. Meanwhile, spatial attention focuses on key regions in the image and strengthens target localization precision. This fused attention design increases model precision and robustness. In complex backgrounds, it helps the model recognize targets more effectively. The efficient structure of the C2 PSA module maintains a strong performance while optimizing computational efficiency. It suits various deep learning tasks, especially in scenarios with fine feature extraction and precise localization requirements. 4. The upsampling module offers significant advantages in deep learning by effectively increasing the resolution of feature maps and enhancing the model's ability to capture fine-grained details. The upsampling module restores spatial information by performing upsampling on low-resolution feature maps. It improves the model's capacity for fine-grained processing in tasks such as object detection and semantic segmentation. This module suits high-resolution output scenarios, such as generative adversarial networks (GANs) and multi-scale feature fusion tasks. At the same time, the upsampling module increases model efficiency under limited computational cost. It improves performance in complex tasks and supports various deep-learning applications related to image restoration and feature expansion. 5. The Detect module processes the features extracted by the backbone network and generates final predictions, including class labels, bounding boxes, and object confidence  
Figure 4: Structure of the SFA module. scores.