## 3 Experimental Design and Results
### Experimental Results and Analysis  
In this study, a baseline model was constructed, incorporating the DownC downsampling module, AFAM, and specialized layers for small object detection. The dataset in this study was divided into a training set and a validation set, distributed at a ratio of 8:2. As shown in Figure 8, in Figure 8 displays the loss function graphs for the training set, while Figure 8 shows the loss graph for the validation set. box _loss represents the loss function value for the model in locating targets, obj_loss indicates the loss function value for predicting the presence of targets, and clc_loss denotes the classification loss, measuring the model's performance in classifying targets. In Figure 8, all loss functions decrease and stabilize over the course of training rounds, indicating gradual improvement in model performance. Although there are some fluctuations in Figure 8, \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Configuration Details** \\ \hline GPU & RTX 4090(24 GB) \(\times\) 2 \\ CPU & 24 vCPU Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10 GHz \\ Operating System & Ubuntu 20.04 \\ CUDA & 11.3 \\ cuDNN & 8.2 \\ PyTorch & 1.10.0+cu113 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental platform configuration. The GPU configuration includes two RTX 4090 cards, each with 24 GB of memory. The CPU used is a 24 vCPU Intel(R) Xeon(R) Platinum 8352V running at 2.10 GHz. The operating system is Ubuntu 20.04, with CUDA version 11.3 and cuDNN version 8.2. PyTorch version 1.10.0+cu113 is used as the deep learning framework. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration Details** \\ \hline Epoch & 500 \\ Number of Categories & 2 \\ Batch size & 8 \\ Image size & 640 \(\times\) 640 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameter settings. The model was trained for 500 epochs. There are 2 categories in the dataset. The batch size used during training is 8, and the input image size is 640 \(\times\) 640 pixels. the overall trend of the loss function is downward, suggesting good generalization of the model. Figure 9 illustrates the detection results of the baseline model. In Figure 9a, all cars and pedestrians in the images are successfully detected with confidence scores exceeding 90%. In Figure 9b, while some distant and smaller targets, such as vehicles and pedestrians, are present, most of them are successfully identified. Only a small portion of these targets are not detected, yet the overall confidence scores remain high. In Figure 9c, the detection results for an image consisting entirely of cars are shown. It is observed that the two cars furthest from the camera were not successfully detected, while the remaining cars were detected with high confidence. In Figure 9d, pedestrians are the dominant class. All pedestrians were successfully detected with confidence scores above 79%, indicating that there is still room for improvement in detection performance. The baseline model data presented in Table 3 show performance variations across different categories. The overall precision is 91.3%, with the Car category achieving a precision of 92.1% and the Pedestrian category at 90.5%. In terms of recall, the overall rate is 83.6%, with a higher 90.1% for the Car category and 77.2% for the Pedestrian category. For the mAP50 metric, the model scores 92.1% overall, with the Car category at 95.9% and the  
Figure 8: Baseline loss function graphs. (**a**) Training set loss. This graph shows the training loss for box, object, and class predictions over 500 epochs. (**b**) Validation set loss. This graph shows the validation loss for box, object, and class predictions over 500 epochs. Figure 9: Detection results of the baseline model. (**a**) Close-up view. Detection results of large targets in close-up scenarios. (**b**) Distant view. Detection results of small targets in distant scenarios. (**c**) Car-dominant Image. This image predominantly contains cars, with only a few instances not being detected.