# DP-YOLO: Effective Improvement Based on YOLO Detector
## 4 Experiment
### Impact of PSA on Label Assignment  
Suitable expansion ranges are very important in PSA, and we relate the radius of the expansion range to the side lengths of the grid and denote the multiplicative relationship of its size by \(r\). Then we conducted a test to determine the optimal value \(r\) of the PSA, which involved comparing the detection effect of the PSA as a label assignment at various  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **\#Params** & **GFLOPs** & **FPS** & \(AP\) & \(AP_{\text{50}}\) & \(AP_{\text{75}}\) & \(AP_{s}\) & \(AP_{m}\) & \(AP_{l}\) \\ \hline YOLOv5s & **7.2M** & **16.4** & 75 & 38.0 & 57.9 & 40.8 & 22.6 & 43.0 & 48.8 \\ +PSA & 7.2M & 16.4 & 75 & 38.1 & 58.1 & 40.9 & 21.5 & 43.3 & 49.0 \\ +DYB & 7.6M & 17.7 & 69 & 41.1 & 60.8 & 44.4 & 24.4 & 45.9 & **54.4** \\ +ALL & 7.6M & 17.7 & 69 & **41.2** & **61.3** & **44.4** & **24.8** & **45.9** & 53.9 \\ \hline \hline \end{tabular}  
* The numbers in bold indicate the optimal value.  
\end{table}
Table 4: Comparison experiments on COCO2017.  
Figure 5: Comparison of DP-YOLO and YOLOv5s (**a**) and comparison of the effect of PSA with different values \(r\) of PSA on training (**b**). COCO AP val denotes \(AP\) as the metric measured on the 5000-image COCO2017 val dataset over various inference sizes from 256 to 640. GPU Speed measures average inference time per image on COCO2017 val dataset using a GeForce RTX 3090 instance with a batch-size of 64.  
values of \(r\) of training on COCO2017. Although the model has not yet fully converged at this point, it is sufficient to demonstrate the impact of different values \(r\) on training. The results are presented in Figure 5b, where we can see that the detection effect reaches its best when \(r\) is set to 1. At this time, the PSA has the closest decision space to the initial method and the difference between them is shown by Figure 6. When the value of \(r\) is set to 0.5 or below, it signifies the abandonment of sample augmentation, and the results will be as suboptimal as expected.  
Table 5 illustrates the comparison of positive samples in the COCO2017 dataset between the initial label assignment stage of YOLOv5 and the label assignment with PSA. Detection layers 1, 2, and 3 correspond to the three different scales of detection layers following the neck structure shown in Figure 4, catering to small, medium, and large objects, respectively. The results reveal that PSA leads to an overall increase of approximately 5% in the number of positive samples. Moreover, experimental evidence demonstrates that the overall quality of the samples also improves, as indicated in Table 4. The introduction of the new sample matching mechanism ensures that the ground truth, positioned at different locations within the grid, attains a more appropriate combination of anchors for simulation. At the same time, the PSA method will still lead to randomness due to the influence of the target location on the determination of positive samples, which will make it more effective for medium and large targets. Consequently, the model benefits from a superior learning target, facilitating enhanced effectiveness during training and making it a valuable addition to the YOLOv5 framework.