## 4 Example Verification
### Experimental Result Analysis  
Based on the model-training process, the improved YOLOv5s model was trained and evaluated on the validation dataset. Taking the dataset of construction workers not wearing safety protective equipment (safety helmets, reflective vests) as an example, the model achieved the following average precision (AP) values: 93.8% for helmet detection, 83.0% for vest detection, and 91.3% for head detection. The mean average precision (mAP) of the model was 89.3%. The detailed verification results of the improved YOLOv5s model are presented in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Number of Markers (pieces)** & **AP (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline Helmet & 6586 & 93.8 & 92.8 & 89.5 \\ Vest & 870 & 83 & 80.5 & 79.4 \\ Head & 13,576 & 91.3 & 89.1 & 92.4 \\ All & 21,032 & 89.3 & 87.5 & 87.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The verified results of the improved YOLOv5s model. In addition, this paper added a study comparing with other models, and the results are shown in Table 4.  
Although the Faster R-CNN model slightly outperforms this model in terms of single-frame accuracy (P = 88.5% vs. 87.5%), its inference speed of 17 FPS does not meet real-time monitoring requirements. While YOLOv3 and the original YOLOv5s models are faster, their mean average precision (mAP) drops by 2.7% and 2.2%, respectively, with a significant decline in the recall rate, which could lead to missed detection of critical safety hazards. In contrast, this study achieved a 2.2% increase in mAP, while moderately sacrificing speed, aligning more closely with the core principle of \"it is better to have a false alarm than to miss a detection\" in construction safety scenarios. To evaluate the impact of the three proposed improvements on the original YOLOv5s model, this study conducted ablation experiments to verify whether these enhancements contribute to the model's performance. The experiments involved progressively introducing each improvement, allowing for a comparison of the performance of the models with different configurations. The experimental procedure mirrored that of model training, with each modification tested on the same dataset and using identical training parameters. The results of the ablation experiments are presented in Table 5.  
To quantify the significance of performance improvements, we conducted five independent trials for each configuration and performed paired \(t\)-tests between successive model variants. The 2.2% mAP improvement from YOLOv5s to YOLOv5s-ESC showed statistical significance (t(4) = 3.28, \(p\) = 0.015) with a 95% confidence interval [0.8%, 3.6%], confirming the CA mechanism's substantive contribution beyond random variation. Similarly, the 4.3% recall boost in YOLOv5s-ES versus baseline achieved \(p\) < 0.001 (t(4) = 5.41, CI [2.9%, 5.7%]), validating the detection layer's critical role in reducing false negatives. Notably, while the initial EIoU incorporation (YOLOv5s-E) demonstrated limited absolute gains (\(\Delta\)mAP +0.4%), its interaction effects with subsequent modifications yielded compounded benefits--the full YOLOv5s-ESC configuration showed a significant improvement over baseline (\(\Delta\)mAP +2.2%, \(p\) = 0.0032, CI [1.1%, 3.3%]). The architectural evolution from YOLOv5s to YOLOv5s-ESC demonstrated a nonlinear relationship between performance gains and computational costs, particularly critical for edge deployment scenarios. While the full ESC configuration achieved a 2.2% mAP improvement (87.1%\(\rightarrow\)89.3%) over baseline, this came at a 25% FPS reduction (56\(\rightarrow\)42) on equivalent hardware--equivalent to a 0.88% mAP gain per 5 FPS loss. The detection