## 5 Experimental Results and Analysis
### Comparative Experiment and Analysis of Results  
In this study, the MEAG-YOLO model was evaluated alongside similar models. The results are shown in Table 3.  
Table 3 and Figure 14 highlight significant improvements in crucial performance indicators, including mAP, accuracy, recall rate, and F1 score, for the enhanced model. SSD surpasses faster R-CNN in detection accuracy by directly detecting objects on multi-scale feature maps, adeptly managing targets of various sizes. SSD concurrently processes multi-scale feature maps, streamlining the detection workflow. It increases detection efficiency by directly predicting categories and bounding box offsets with anchor boxes of different sizes on each feature map layer. The YOLO series outstrips both faster R-CNN and SSD in performance metrics. YOLOv3 employs a multi-scale prediction mechanism to maintain high accuracy across targets of various scales, enhancing the overall mAP. It incorporates Darknet-53, a complex feature extractor with residual connections. These connections prevent gradient vanishing and enhance feature representation. YOLOv5's improved architecture allows adaptive and automatic adjustments. It employs an effective feature extraction network and advanced multi-scale prediction, enhancing adaptation to various target shapes and sizes. YOLOv7-tiny boasts the innovative E-ELAN network structure, refining feature extraction and utilization to boost detection accuracy. It employs cross-scale feature fusion to enhance feature capture and integration. Additionally, it utilizes efficient data augmentation and optimization algorithms during training, improving generalization and robustness. Last but not least, MEAG-YOLO integrates MSCA and optimizes the detection head. Through the innovative design of the EC2f structure and GhostConv module integration, feature extraction and fusion efficiency. It maintains a high mAP value, achieving a balance between detection accuracy and lightweight design.  
Figure 13: Comparison of precision with different model FLOPs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **mAP (\%)** \\ \hline Faster R-CNN & 89.7 & 87.8 & 88.7 & 88.6 \\ SSD & 90.6 & 89.4 & 89.9 & 90.1 \\ YOLOv3 & 92.3 & 90.2 & 91.2 & 91.8 \\ YOLOv5 & 94.2 & 91.5 & 92.8 & 93.5 \\ YOLOv7-tiny & 94.9 & 92.3 & 93.6 & 94.3 \\ MEAG-YOLO & **98.4** & **94.2** & **96.3** & **96.5** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of object detection models.  
Finally, Figures 15 and 16 provide a comparative analysis of the detection results between the YOLOV8n and MEAG-YOLO models.  
Figure 14: Comparison of experimental results.  
Figure 15: Comparison of model detection results. (**a**) YOLOv8n’s detection results; (**b**) MEAG-YOLO’s detection results. (**c**) YOLOv8n’s detection results; (**d**) MEAG-YOLO’s detection results.  
Figure 15a shows YOLOv8n correctly identifying persons, safety helmets, ordinary gloves, and armbands, with confidence levels of 0.77 for helmets and 0.56 for gloves. Figure 15b highlights MEAG-YOLO's improvements in detecting small items in complex settings, increasing confidence to 0.83 and 0.68 for helmets and gloves, respectively. In Figure 15c, YOLOv8n accurately detects people, helmets, and gloves. But this model incorrectly identifies a background object as a \"person\". The yellow dashed box highlights this situation. Figure 15d shows MEAG-YOLO accurately identifying targets and avoiding false alarms.  
Figure 16a,c compare YOLOv8n's and MEAG-YOLO's detection abilities. Figure 16b,d zoom in on YOLOv8n's results and MEAG-YOLO's results, respectively. It shows that MEAG-YOLO correctly identified the missed armband, thereby addressing issues in the original model.