## 4 Experiment and Analysis  
In this chapter, we introduce the process of model training, verification, and testing on the dataset, including the architecture of the model and the data enhancement technology used in the training process. Figures 7 and 8 show the training process and evaluation flow chart of the PASCAL-VOC dataset and the MAR20 dataset. The PASCAL-VOC dataset [36], with the full name Pattern Analysis, Statical Modeling and Computational Learning, is a large-scale data set of object detection, segmentation, key point detection, and caption. VOC is divided into two versions: voc2007 and voc2012, with VOC2007 including 9903 images divided into training sets (5011 images) and test sets (4952 images) [37; 38]. In addition, the 2012 version has added 17,125 additional training images as a supplementary training set. Common usage methods include:  
1. Only train with VOC2007 and test with VOC2007. 2. Only train with VOC2012 and test with VOC2012. This usage is rarely used, as it is commonly used in conjunction with VOC2007. (Train with VOC2007's train+val and VOC2012's train+val, and then use VOC2007's test. This usage is commonly seen in papers as 07+12, and researchers can test the results on VOC2007 themselves, because VOC2007's test is public). 3. Use VOC2007's train+val+test and VOC2012's train+val training, and then use VOC2012's test, which is commonly seen in papers as 07+12. This method needs to be submitted to the VOC official server for evaluation results, as the VOC2012 test has not been published. Figure 6: PNOC network structure. Figure 7: Application of PASCAL-VOC dataset in our network. The method used in this experiment is 07 + 12. The experiment has 20 classes and contains 22,136 training images (voc2007 and 2012 trianval), as well as 4952 test images (voc2007 test). We chose the average precision (AP) and average recall (AR) as evaluation indicators. The specific experimental data of the proposed algorithm and other comparison algorithms in the PASCAL-VOC dataset are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **mAP** \\ \hline \multicolumn{4}{c}{Two-Stage Network} \\ \hline OHEM & VGG16 & 1000 \(\times\) 600 & 74.6 \\ IPG RCNN & IPGNet101 & 1000 \(\times\) 600 & 84.8 \\ CoupleNet & ResNet101 & 1000 \(\times\) 600 & 82.7 \\ FPN-Reconfig & ResNet101 & 1000 \(\times\) 600 & 82.4 \\ Fast-RCNN & Faster RCNN101 & None & 68.4 \\ Faster-RCNN & Faster RCNN101 & None & 70.4 \\ \hline \multicolumn{4}{c}{One-Stage Network} \\ \hline SSD300 & VGG16 & None & 72.4 \\ SSD512 & VGG16 & 512 \(\times\) 512 & 74.9 \\ YOLO & GoogLeNet & 448 \(\times\) 448 & 57.9 \\ YOLOv2 & Dark-Net19 & 352 \(\times\) 352 & 44.0 \\ YOLOv3 & Dark-Net53 & 320 \(\times\) 320 & 51.5 \\ YOLO-ultralytics & YOLOv8 & 640 \(\times\) 640 & 75.1 \\ Ours & YOLOv8 & 640 \(\times\) 640 & 75.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental data of this algorithm and other algorithms are compared in the PASCAL-VOC dataset (IOU = 0.5) [1]. Figure 8: Application of MAR20 dataset in our network. The statistical data in the table reflect an objective fact: the algorithm proposed in this paper demonstrates excellent performance in recognizing small targets within the PASCAL-VOC dataset. Compared to some of the two-stage networks, despite having a smaller input size, our algorithm exhibited a significant increase in accuracy. For example, in comparison with the OHEM algorithm, this results in an improvement of 1.2%. Among the enumerated one-stage network methods, our algorithm demonstrated a more precise recognition effect compared to the closest-performing SSD512 algorithm across different backbone frameworks, thus leading to an enhancement of 0.9%. The reason SSD512 is considered the method closest in performance to ours is because of two main factors: First and foremost, both algorithms belong to the category of one-stage methods, which enhances the credibility of the comparison.