## 5 Experimental Results and Analysis
### Personal Protective Equipment Image Detection  
This study conducted comparative experiments between the proposed algorithm and several well-performing detection algorithms to evaluate the effectiveness of the SFA-YOLO algorithm in detecting personnel safety attire during inspections by intelligent factory inspection robots. The experiments are performed using a unified dataset and identical configuration. The experimental results are shown in Figure 5. The scores displayed in the detection boxes indicate confidence scores. These scores reflect the reliability of object recognition in the image and help assess the performance of the proposed algorithm. The confidence score reflects the model's estimation of the probability that a specific object exists. The value ranges from 0 to 1. A higher confidence score indicates greater certainty in the model's prediction. In the nine subfigures in the three left columns of Figure 5, successful detection cases are presented, indicating a high recognition rate under sufficient lighting and clear contrast between the background and the target. The six subfigures in the two right columns of Figure 5 illustrate scenarios under other lighting conditions. When night-time illumination is sufficient, the model still performs reasonably well in recognition. However, under insufficient night-time lighting, the confidence scores decrease, accompanied by partial missed detections and a small number of false positives. Moreover, in cases where the image quality is poor, the detection model may encounter difficulties when multiple helmets or vests appear close to each other in the same frame. In such situations, the model may mistakenly merge two closely positioned objects into a single detection, leading to false identification. A higher recall can effectively reduce the number of missed detections. This \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Version** \\ \hline OS & Ubuntu 16.04 LTS \\ CPU & Intel(R) Core(TM) i7-11800H @ 3.20 GHz \\ RAM & 32 GB \\ GPU & GeForce RTX 3060 \\ Driver & 456.19.01 \\ CUDA & 11.3.1 \\ python & 3.9.18 \\ torch & 1.10.1+cu11 \\ torchvision & 0.11.2++cu111 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental server operating environment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline epochs & 500 & max\_det & 300 \\ patience & 100 & lr0 & 0.01 \\ batch & 32 & lrf & 0.01 \\ imgsz & 640 & dropout & 0.0 \\ workers & 4 & optimizer & SGD \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SFA-YOLO experiment hyperparameter configuration. ensures that fewer true instances are overlooked. It helps avoid false alarms and reduces the need for unnecessary manual intervention. This improves overall system reliability. As shown in Figure 6, during the 500-epoch training process, the SFA-YOLO model approaches convergence within approximately the first 100 epochs. Precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 all approach 90%, demonstrating the SFA-YOLO model's strong performance. Figure 5: Safety attire detection experiment. Figure 6: SFA-YOLO algorithm training process. The meanings of the other metrics in Figure 6 are as follows:  
Box_loss: Bounding box loss. It measures the difference between the predicted and ground truth bounding box positions in object detection models. Cls_loss: Classification loss. It measures the accuracy of the object detection model in classifying different classes. Dfl_loss: Distribution Focal Loss. It assists box_loss by providing additional information. It optimizes the probability distribution of bounding box positions, further refining and improving the model's accuracy in locating the boxes. Each result receives a category label in object detection tasks to reflect the model's classification performance across different classes. After training the SFA-YOLO model, the confusion matrix based on the validation set comprehensively evaluates overall performance.