## 5 Experimental Results and Analysis
### Performance Comparison of Different Detection Models  
To verify the effectiveness of the proposed method for safety attire detection and to compare its performance, ViT, YOLOv7-tiny, YOLOv5s, YOLOv5n, ViT, CBAM,-YOLO, YOLOv8n, and YOLOXnano are trained for 500 epochs under the same dataset and experimental configuration. The results are listed in Table 4.  
As regards detection performance, SFA-YOLO reaches 149 FPS, and the processing time per image is 6.4 ms, far below the cycle of the robot control system (16 ms). This result confirms that SFA-YOLO meets the real-time requirements in high-speed processing scenarios. Compared with other lightweight networks, SFA-YOLO achieves clear advantages in precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS].  
The core idea of both CBAM-YOLO and SFA-YOLO focuses on enhancing the network's attention to important features during the feature extraction stage, though they adopt different approaches. CBAM-YOLO enhances the model's focus on key regions and features, but its adaptability to target occlusion or complex backgrounds is relatively poor. In this paper, the proposed SFA-YOLO builds upon the traditional YOLO framework and incorporates improvements to address issues such as target occlusion and complex  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **FPS** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv5n & 256 & 84.1\% & 72.2\% & 76.3\% & 37.8\% \\ YOLOv5s & 156 & 85.6\% & 78.1\% & 81.2\% & 42.1\% \\ YOLOv7-Tiny & 172 & 87.2\% & 72.4\% & 78.2\% & 40.8\% \\ CBAM-YOLO & 123 & 86.3\% & 77.8\% & 81.7\% & 47.6\% \\ YOLOv8n & 259 & 84.5\% & 72.6\% & 76.9\% & 38.1\% \\ YOLOX-Nano & 271 & 81.2\% & 69.6\% & 73.1\% & 34.9\% \\ ViT & 87 & 82.9\% & 81.0\% & 83.4\% & 48.4\% \\ SFA-YOLO & 149 & 89.3\% & 83.7\% & 89.4\% & 50.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance comparison of the different algorithms.  
Figure 8: Confusion_matrix of SFA-YOLO.  
backgrounds in safety attire detection. The experimental results demonstrate that, under sufficient lighting, the model can accurately detect even in the presence of occlusion. SFA-YOLO introduces PCONV and CGLU modules, which emphasize feature representation while maintaining a reasonable detection speed. Based on its performance on the safety attire detection dataset, SFA-YOLO shows greater suitability for this task.  
The SFA-YOLO model has 2,240,356 parameters and achieves 5.7 GFLOPS. In practical detection tasks, the inspection program runs on a cloud server equipped with a GPU more powerful than the Nvidia RTX 3060. During detection, the GPU memory usage is about 2 GB.  
In real-world detection tasks, inspections are usually conducted near well-lit production sites. For the few relatively dark scenes, light sources can be added to the inspection robot to improve image quality. Additionally, in intelligent factories, the division of labor in production environments is clear, and situations where personnel stand very close to each other are rare. As a result, such extreme conditions are rarely encountered in actual detection tasks. The detection accuracy remains at the same level as the experimental results.  
When the detection model is applied to scenarios not covered by the training dataset, it may experience a decline in detection accuracy, along with increased false positive and false negative rates. The model may also become more sensitive to noise and lighting variations in unfamiliar environments, further reducing detection performance. In this case, additional images can be captured from the actual deployment site, annotated accordingly, and added into the training dataset for improved model robustness.