## 4 Experiment and Analysis
From the results, it is evident that our algorithm's improvement is indeed significant. However, it is important to note that the input size of the SSD512 algorithm is smaller than that of our algorithm, thus giving our algorithm an advantage. Therefore, based on the conventional way of comparing state-of-the-art methods, disregarding the input image size and focusing solely on the results, our algorithm is considered advanced. On the other hand, the reason SSD512 is considered the method closest in performance to ours is also attributed to its smaller input size compared to our algorithm. Additionally, since our paper is derived from the YOLO-ultralytics framework, it falls under the same line of thought as YOLO-ultralytics. However, it is crucial to mention that the method mentioned in the paper as the closest is based on a completely different approach or computational method. Therefore, despite YOLO-ultralytics outperforming SSD512, the authors still refer to SSD512 as the method closest in performance to ours in the conclusion. The reason this paper innovates on top of YOLOv8 is that even the unoptimized YOLOv8 algorithm still demonstrates advancement in accuracy compared to previous versions. Therefore, this paper chooses to build upon the already excellent framework and innovate further, thus aiming to advance accuracy. The conclusions drawn from the comparison are as follows: In comparison with other frameworks within the YOLO series, our algorithm exhibits varying degrees of improvement, thus notably achieving a 0.7% enhancement compared to the YOLOv8 algorithm. Based on these data, the performance enhancement is primarily attributed to the design of the small target feature module DDU and the integration of the novel attention mechanism PNOC algorithm that fuses features. For this purpose, we conducted corresponding ablation experiments under the experimental conditions of 100 epochs, with image dimensions fixed at 640 \(\times\) 640 and other parameters unchanged. The experiments were aimed at verifying the impact of the DDU module and PNOC module on accuracy, recall, \(mAP50\), and \(mAP(50\text{-}95)\). The results, as shown in the Table 4, demonstrate the effectiveness of the DDU and PNOC modules. Through the data statistics of the ablation experiment, it can be concluded that the designed algorithm does have corresponding effects, which are specifically as follows: In terms of small target detection item \(mAP50\) alone, the accuracy of adding DUU and PNOC modules increased by 0.3% over the module only adding DUU, 1.1% over the module only adding PNOC, and 0.7% over the baseline. It should be noted that the results of Table 4 are based on Table 3, but Table 3 shows the values determined without any change in accuracy after multiple iterations, while Table 4 shows the comparative data of the relevant ablation experiments conducted after 100 epochs. The number of epochs for this experiment was set to 100, and through experimental comparisons, it was observed that the network performance significantly improved with \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P** & **R** & _mAP50_ & _mAP50-95_ \\ \hline YOLOv8 & 71 & 59.2 & 66 & 46.5 \\ YOLOv8+DDU & 72.4 & 59 & 66.4 & 46.9 \\ YOLOv8+PNOC & 70.6 & 58.4 & 65.6 & 46.1 \\ YOLOv8+DDU+PNOC & 71.9 & 59.3 & 66.7 & 47.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ablation experiment of the algorithm in this paper. the addition of the DDU and PNOC modules compared to the original code or the case of adding only one new module. Additionally, in this experiment, it was noted that the results of adding only the PNOC module decreased by 0.4 compared to the results of the original code. This suggests that attention mechanisms are more suitable for being used in conjunction with sets that have clear features of small targets. Otherwise, the meaningless stacking of attention mechanisms on the original feature set may have a counterproductive effect on the results.