# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 4 Experiments
### Analysis of Experimental Results  
To verify the suitability of the GhostNet algorithm as a baseline network for pedestrian detection under rainy weather conditions, we conduct ablation experiments. By comparing the performance of the GhostNet network with and without the compact bilinear pooling algorithm, we assess the combined effect of integrating the compact bilinear pooling algorithm with the GhostNet network. The experimental setup is outlined in Table 2.  
First, from Table 2, we can conclude that GhostNet as the backbone network of YOLOv5-x has the smallest model size and the best FPS performance. mAP performance is significantly better than other series of backbone networks, which proves that the GhostNet network is more suitable for the detection task in this paper. The CBP-GNet model parameters increase by 2.8 M compared with the original GhostNet network, but the mAP improves by 2.2%. The CBP-GNet network can improve the detection accuracy with only a small computational cost, proving that the proposed CBP-G bottleneck layer has good performance. The experimental results demonstrate that GhostNet performs better than other main networks as a baseline network for pedestrian detection under rainy weather conditions and that the GhostNet network with the compact bilinear pooling algorithm outperforms the original GhostNet network. To demonstrate that the proposed Simple-BiFPN feature fusion network is better suited as the feature fusion network for the current detection task, we compare its performance to that of PANet and NAS-FPN networks. The experimental results are shown in Table 3 (the backbone network utilized in our approach is CBP-G): \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **mAP (\%)** & **Model Size (Mb)** & **Model Scaling Ratio** & **FPS** \\ \hline - & 49.91 & 86.1 & - & 30.5 \\ GhostNet & 43.9 & 21.3 & 75.2 & 48.6 \\ CBP-GNet & 50.1 & 24.1 & 72 & 47.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Backbone network comparison experiment. The experimental results show that Simple-BiFPN works better in terms of mAP, FPS, and model reduction ratio performance and that the proposed feature fusion network has a smaller model and better detection performance, which can perform well in complex weather pedestrian detection scenarios. To validate the effectiveness of integrating the CBAM attention mechanism between the backbone network and feature fusion network, as well as integrating the REP module between the feature fusion network and the YOLO detection head, we design comparative experiments, as shown in Table 4.  
Table 4 shows the comparison experiments of adding the CBAM module and the REP module to the CBP-GNet + Simple-BiFPN network structure. The experiments prove the effectiveness of adding the CBAM and REP modules; the network performance is improved by 0.48% with a 1.48 M increase in computational cost. The experimental results demonstrate that the integration of the CBAM attention mechanism and the REP module can significantly improve the performance of the network for rainy weather detection tasks. This finding validates the effectiveness of the proposed improvements. To evaluate the effectiveness of the RSTDet-Lite algorithm, we conduct a performance comparison on the RainDet3000 dataset and select some mainstream algorithms for comparison, including YOLOv4, YOLOv5x, yolov7, and the algorithms proposed by [PERSON] [14], [PERSON] [16], and [PERSON] [21] as shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network (CBP-** & **mAP (\%)** & **Model Size (Mb)** &
\begin{tabular}{c} **Model Scaling** \\ **Ratio** \\ \end{tabular} & **FPS** \\ \hline - & 52.99 & 11.4 & 86.8 & 48.8 \\ +CBAM & 53.09 & 11.4 & 86.8 & 49.8 \\ +CBAM + REP & 54.47 & 12.3 & 85.7 & 49.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison experiments of different modules.