# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 4 Experiments and Analysis
### Ablation Experimental Analysis  
This work is trained without using the initialized weights to test the performance of the upgraded YOLOX-s model and better understand the detection effect of each improved approach. To ensure the effectiveness of the enhanced method in the experiments, epochs are set to 100. The batch size is set to 32. The input image size is 640 \(\times\) 640. The improved scheme is depicted in Table 1, and the P, R, F1, mAP, and FPS indicators derived from a comparison of experiments conducted with various improved approaches are provided in Table 2. Figure 4 depicts a more visual representation of the training effect of each improvement approach on mAP boosting.  
The structural enhancement techniques for YOLOX-s are detailed in Table 1. In Table 1's HorNet method, the HorNet module substitutes the Res8 module in the original Darknet53 to improve the network's feature extraction capabilities, detection accuracy, and speed. In the HorNet+BiFPN scheme, the FPN structure of Neck is replaced with top-down and bottom-up bidirectional weighted feature fusion network structures, based on theimproved HorNet method, to address the issue of partial speech information loss during feature fusion and enhance model robustness. In the HorNet+BiFPN+SIOU scheme, the original loss function is replaced with the SIOU loss function to increase the regression accuracy of the target and the detection accuracy in the situation of dense occlusion to improve the model detection accuracy further.  
According to the improved results demonstrated in Table 2, we analyze the ablation experiment with an input image size of 640 \(\times\) 640, and we can see that the results of P, R, F1, and mAP are relatively low in the original YOLOX-s algorithm. After using the HorNet module instead of the original Res module, using the self-attention mechanism to make the network focus on the information that needs attention, the values of P, F1, and mAP are improved by 0.6%, 0.8%, and 1.5%, respectively. Compared with the unimproved model, the HorNet module can effectively improve the detection accuracy of the model. The HorNet+BiFPN scheme improves the values of P, F1, and mAP by 3.1%, 0.9%, and 3.2%, respectively, which shows a significant improvement of the network effect after the improved two-way weighted feature fusion by experimental data. In the HorNet+BiFPN+SIOU scheme, P, F1, and mAP are increased by 1.1%, 1.3%, and 0.7%, respectively, while keeping the detection speed unchanged. The SIOU loss function is introduced to solve the problem of low detection accuracy caused by severe occlusion, and the effectiveness of this method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **HorNet** & **BiFPN** & **SIOU** \\ \hline YOLOX-S & & & & \\ HorNet & \(\surd\) & & & \\ HorNet + BiFPN & \(\surd\) & \(\surd\) & & \\ HorNet + BiFPN + SIOU & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various module combinations are displayed; “\(\surd\)” indicates the selected module.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **mAP** & **FPS** \\ \hline YOLOX-S & 90.8 & 91.2 & 91.0 & 90.1 & 37.5 \\ HorNet & 91.4 & 92.3 & 91.9 & 91.6 & 36.4 \\ HorNet + BiFPN & 94.5 & 91.1 & 92.8 & 94.8 & 39.3 \\ HorNet + BiFPN + SIOU & 95.6 & 92.4 & 94.1 & 95.5 & 41.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of several improved ablation methods.  
Figure 4: mAP curves of different improvement methods.  
is verified. This program also adopts the final scheme, and the HorNet+BiFPN+SIOU scheme is renamed HBSYOLOX-s.  
Figure 4 depicts the mAP training process, with the horizontal axis representing the training batches and the vertical axis showing the mAP change results to compare each approach's improvement effect visually. Figure 4 depicts the success of each improvement approach, where the green curve shows the HBSYOLOX-s scheme, and the improvement is attributed to the other methods.