# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 4 Experiments
### Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Fusion** & **mAP (\%)** & \
\begin{tabular}{c} **Model Size** \\ **(Mb)** \\ \end{tabular} &
\begin{tabular}{c} **Model Scaling** \\ **Ratio** \\ \end{tabular} & **FPS** \\ \hline PANet & 50.1 & 24.1 & 72 & 47.9 \\ NAS-FPN & 51.2 & 25.9 & 69.9 & 34.6 \\ Simple-BiFPN & 52.99 & 11.4 & 86.8 & 48.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature fusion network comparison experiments. The results demonstrate the outstanding performance of RSTDet-Lite in multiple aspects. Model performance: RSTDet-Lite excels in terms of mean average precision (mAP), achieving a superior score of 54.47%, surpassing YOLOv7 (50.9%) and PVformer (52.44%). This highlights the superiority of RSTDet-Lite in terms of object detection capability. Inference speed: RSTDet-Lite exhibits remarkable speed, achieving a frame rate of 49.8 frames per second (FPS). This high FPS is crucial for real-time applications where faster processing speed is of paramount importance. Model size: notably, RSTDet-Lite maintains a compact model size, occupying only 12.3 megabytes (Mb), significantly smaller than many other models, reducing storage and memory requirements. Finally, RSTDet-Lite achieves an AP60 value of 51.68%, surpassing other detection algorithms. This result indicates that the model performs better at high recall rates, effectively capturing more positive class targets and reducing false negatives. These results underscore the effectiveness of RSTDet-Lite as an advanced and efficient object detection solution, providing a striking balance between accuracy, speed, model size, and recall rate. It represents a promising choice for practical applications where these factors are critical. To further demonstrate the effectiveness of RSTDet-Lite in challenging scenarios, we conduct comparative experiments on the BBD100K-pedestrian dataset, which includes pedestrian images captured under various challenging weather conditions. Specifically, we select YOLOX, PVformer, CA-MobileNetv2-YOLOv4, and our proposed RSTDet-Lite algorithm for the comparative experiments. The detailed experimental results are presented in Table 6.  
The results indicate that our innovative RSTDet-Lite model performs exceptionally well on the challenging BBD100K-pedestrian dataset, outperforming YOLOv5-x by a significant margin. Our algorithm achieves a recall rate of 0.9546, which is 0.0623 higher than YOLOv5-x, and a mAP of 49.89%, surpassing the other algorithms. This suggests that RSTDet-Lite exhibits exceptional robustness in the presence of complex weather conditions. Notably, RSTDet-Lite achieves high performance while employing minimal computational resources, underscoring its efficiency. Additionally, our network demonstrates strong robustness, further validating its efficacy. These findings collectively demonstrate the compelling effectiveness of our proposed RSTDet-Lite, which represents a promising solution for pedestrian detection in challenging weather conditions. In the final section of our experiments, we present a visual comparison of the pedestrian detection results to showcase the superior performance of our proposed algorithm. Figure 12b displays the detection outcomes achieved by the original YOLOv5-x model, while Figure 12c show cases the detection results obtained by our algorithm. These images offer a side-by-side illustration of the pedestrian detection performance of both methods in the same scenarios. Through this visual comparison, it becomes evident that our algorithm significantly enhances pedestrian localization and detection accuracy. Figure 12 illustrates the experimental comparison results.