## 5 Experimental Results and Analysis
### The Generalizability of the SFA-YOLO Method  
The previous experiments have already demonstrated the superior performance of the SFA-YOLO algorithm on the safety attire detection dataset. The MIMIC-CXR (MIMIC Chest X-ray) dataset is a publicly available collection of chest radiographs developed to facilitate research in medical image processing, disease diagnosis, and intelligent diagnostic systems. It comprises a large volume of accurately labeled X-ray images along with corresponding radiological reports, encompassing a wide spectrum of thoracic conditions, among which pneumonia is a key focus. The dataset includes more than 200,000 chest X-ray images that illustrate various pulmonary diseases such as pneumonia, tuberculosis, and pneumothorax, with most images originating from hospitalized patients. In total, 4194 images related to pneumonia were selected for detection purposes. This section further conducts experiments using a pneumonia detection dataset to evaluate the SFA-YOLO network's generalization capability and analyze its detection performance on other public datasets. We used the open-source MIMIC Chest X-ray pneumonia dataset; the images were re-annotated using the LabelImg tool. As shown in Figure 9, the dataset contains five labels: Pneumonia Bacteria, Pneumonia Virus, Sick, Healthy, and Tuberculosis. To access the open-source MIMIC Chest X-ray pneumonia dataset, visit the following address: [[https://physionet.org/content/mimic-cxr/2.0.0/](https://physionet.org/content/mimic-cxr/2.0.0/)]([https://physionet.org/content/mimic-cxr/2.0.0/](https://physionet.org/content/mimic-cxr/2.0.0/)) (accessed on 1 January 2024).  
Based on the results in Table 6, SFA-YOLO outperforms other YOLO-based algorithms on the pneumonia detection dataset. Although YOLOv7-Tiny and YOLOv5n achieve slightly higher precision, SFA-YOLO shows significantly better results in FPS, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95. SFA-YOLO demonstrates a superior overall performance.  
Higher recall reflects stronger sensitivity of SFA-YOLO to features and reduces missed detections, which holds greater importance in pneumonia diagnosis. Higher [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 indicate that SFA-YOLO maintains more robust detection performance under various conditions. Higher FPS ensures the more excellent practical value of SFA-YOLO in real-time detection scenarios.  
Figure 9: Pneumonia detection dataset.  
SFA-YOLO achieves the best results in recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 on the safety attire and pneumonia detection datasets. Unlike other networks, the FPS consistently remains above 140 and shows no significant variation between the two datasets. These results demonstrate the stability and reliability of SFA-YOLO.  
Previous experiments validate the generalization ability of SFA-YOLO using datasets from different domains. In this study, we also evaluate SFA-YOLO on other datasets from the industrial inspection field. We construct a worker behavior detection dataset and evaluate three behaviors: normal, play, and sleep. The worker behavior detection task shares certain similarities with the safety attire detection task.  
Based on the results in Table 7, SFA-YOLO shows slightly lower precision compared to other classic networks. However, it achieves significantly higher recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95, indicating a superior performance on the employee behavior detection dataset.