## 6 Conclusions  
This paper proposes the SFA-YOLO model based on the Selective Feature Attention (SFA) mechanism to address the issues of low efficiency and high miss rate in safety attire detection for inspection robots in intelligent factories. The proposed model achieves a frame rate (FPS) of 149 and a precision of 89.3% on the safety attire dataset. It balances precision and efficiency and meets the practical requirements of automatic detection in intelligent factory inspection robots. This method improves the C3k2 module and introduces the SFA mechanism. SFA integrates multi-scale feature fusion and a gated feature extraction strategy. PCONV and CGLU form the core of the SFA module. The mechanism enhances feature selection and handles the personal protective equipment dataset's image loss, target overlap, and minor object issues. The experimental results show optimal precision in SFA-YOLO compared to that of classical YOLO models. The model also achieves near-top scores in the recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95. These results confirm strong overall performance. Generalization experiments prove its adaptability across domains. SFA-YOLO meets real-world \begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithms** & **FPS** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv5n & 88 & 96.7\% & 94.7\% & 97.5\% & 77.0\% \\ YOLOv5s & 75 & 95.1\% & 94.7\% & 97.4\% & 82.7\% \\ YOLOv7-Tiny & 103 & 96.8\% & 94.3\% & 97.7\% & 76.6\% \\ CBAM-YOLO & 68 & 96.6\% & 95.1\% & 95.8\% & 84.2\% \\ YOLOv8n & 94 & 97.2\% & 94.9\% & 97.6\% & 77.5\% \\ YOLOX-nano & 101 & 95.3\% & 93.6\% & 95.4\% & 76.3\% \\ ViT & 55 & 93.4\% & 91.8\% & 96.6\% & 89.6\% \\ SFA-YOLO & 143 & 95.8\% & 95.9\% & 97.7\% & 97.6\% \\ \hline \end{tabular}
\end{table} 
Table 6: The performance comparison of the different algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithms** & **FPS** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv5n & 213 & 87.8\% & 83.3\% & 89.8\% & 64.3\% \\ YOLOv5s & 187 & 88.5\% & 83.6\% & 90.1\% & 66.4\% \\ YOLOv7-Tiny & 195 & 90.3\% & 84.2\% & 89.6\% & 65.0\% \\ CBAM-YOLO & 166 & 89.2\% & 82.3\% & 90.6\% & 66.8\% \\ YOLOv8n & 224 & 88.4\% & 84.1\% & 90.3\% & 64.9\% \\ YOLOX-nano & 235 & 85.1\% & 82.4\% & 88.5\% & 63.2\% \\ ViT & 82 & 82.6\% & 84.5\% & 86.6\% & 68.1\% \\ SFA-YOLO & 135 & 85.0\% & 90.4\% & 92.4\% & 71.5\% \\ \hline \end{tabular}
\end{table} 
Table 7: The performance comparison of the different algorithms on the behavior detection dataset. detection demands. The [EMAIL_ADDRESS]:0.95 score still allows for further improvements in the safety attire dataset. In real-world detection tasks, false positives and missed detections occasionally occur. These errors mainly happen under low-light conditions. In such cases, the model struggles to detect targets that resemble the background. It also has difficulty identifying occluded or closely adjacent targets. This limitation results in detection errors. Although this situation is rare, we can mitigate it by adding lighting to the inspection robot. In a factory production environment, personal protective equipment is not limited to helmets and vests; it may also include more comprehensive protective gear such as gloves, goggles, and safety shoes. The detection environment may also involve factors such as smoke, dust, or other conditions that can affect image quality. In our future research, we will address these issues more comprehensively by constructing a more extensive dataset. Additionally, we will incorporate more appropriate attention mechanisms in the model's feature enhancement to further improve the model's usability under these extreme conditions. In response to the false positive and missed detection cases reported by on-site users, we will improve both the dataset and the model to enhance its generalization capability. Conceptualization, R.S. and B.Z. ; methodology, C.W. ; software, R.S. and B.Z. ; validation, R.S. and B.Z. ; formal analysis, C.W.