# Pedestrian Detection Based on Feature Enhancement in Complex Scenes
## 4 Experiments and Analysis
### Ablation Experiments  
To further validate the effectiveness of the pedestrian detection network PT-YOLO proposed in this paper, the following ablation experiments were designed. The training results on the dataset are shown in Table 4. The SE module focused on important features while suppressing irrelevant ones. The BiFPN module enhanced feature fusion. The WIoU loss function reduced regression errors and the coordconv module allowed the network to better perceive the location information in the feature map. As shown in Table 4, we found that with the addition of the SE module, the MAP and precision of the PT-YOLO pedestrian detection network have been improved compared to the benchmark network YOLOv5. The experiments show that the SE module can make the pedestrian detection network pay more attention to the important features and suppress the unimportant features, which in turn improves the feature extraction capability of the pedestrian detection network. In this paper, the MAP is further improved after improving the neck network to the BiFPN module. The experiments show that the network strengthens the feature fusion precision of the pedestrian detection and improves the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Precision\%** & **Recall\%** & **Loss** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]–0.95\%** \\ \hline YOLOv5 & 93.8 & 81.2 & 0.064 & 90.9 & 67.4 \\ YOLOv5 + SE & 94.4 & 81.2 & 0.064 & 91.4 & 70.4 \\ YOLOv5 + SE + BiFPN & 94.4 & 83.1 & 0.060 & 92.2 & 70.0 \\ YOLOv5 + SE + BiFPN + WIoU & 95.2 & 83.1 & 0.055 & 93.0 & 72.2 \\ PT-YOLO (YOLOv5 + SE + BiFPN & 95.2 & 85.2 & 0.054 & 93.4 & 72.8 \\ + WIoU + coordconv) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ablation experiments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Loss Function Types**} & \multicolumn{3}{c}{**Results**} \\ \cline{3-5}  & & **MAP0.5\%** & **MAP0.5-0.95\%** & **Precision\%** \\ \hline YOLOv5 & CIoU & 90.9 & 67.4 & 93.8 \\ YOLOv5 & WIoU & 91.6 & 67.7 & 94.4 \\ PT-YOLO & WIoU & 93.4 & 72.8 & 95.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of loss function results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision\%** & **Recall\%** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]–0.95\%** \\ \hline YOLOv5 & 93.8 & 81.2 & 90.9 & 67.4 \\ YOLOv5 + Swin\_Transformer & 93.8 & 81.1 & 91.4 & 68.1 \\ YOLOv5 + CBAM & 94.2 & 81.9 & 91.4 & 69.0 \\ YOLOv5 + CA & 94.4 & 80.7 & 90.9 & 67.6 \\ YOLOv5 + SE & 94.4 & 81.2 & 91.4 & 70.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different attention mechanisms on the module. precision of pedestrian detection by using the BiFPN module. The optimization of the loss function reduces the regression loss of the network and at the same time improves the MAP of the network. The coordconv module is added to the neck to enable the network to accurately localize the pedestrian's position. In summary, the pedestrian detection network PT-YOLO proposed in this paper combined the SE module, the BiFPN module, the coordconv module, and the WIoU loss function to improve the detection effect of the network, and the precision, recall, and MAP of the detection results were 95.2%, 85.2% and 93.4%. Figure 9 shows the training results of each round. The horizontal axis represents the number of training iterations, while the vertical axis represents the MAP at an IoU threshold of 0.5 and IoU thresholds ranging from 0.5 to 0.95 with a step size of 0.05. Different colored curves represent different networks. In a word, compared to the benchmark network or using specific modules alone, the pedestrian detection network PT-YOLO demonstrates improved performance in pedestrian detection across various scenes. The precision has increased from 93.8% to 95.2%, the recall has improved from 81.2% to 85.2%, and the MAP has risen from 90.9% to 93.4%. Additionally, the loss function has decreased from 0.064 to 0.054.