# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 4 Results
### Performance Evaluation of Different Methods  
It is worth noting that in all of our proposed methods, the MRPE belongs to the lighter modules, which can be easily replaced in the YOLOv9 model. We validated the effectiveness of our introduced MRPE in a wide range of object detection methods, as shown in Table 1. We conducted comparison experiments between the MRPE and two mainstream attention mechanisms, SE and SGE, and we fine-tuned the network structure of SE and SGE to fit YOLOv9's model better, as shown in Table 2, by replacing YOLOv9's modules; the MRPE had about 0.3% performance improvement in the category of rebar. On the other hand, the MRPE had about 0.2% performance improvement in the category of edge rebar. Through our analysis, we found that the MRPE indirectly assigns different weights to different categories, thereby increasing the correlation between targets. This is particularly evident between rebar and edge rebar, two categories that have similar features but different scales. Such correlations are also effective for other categories, such as holes and junction boxes, which are not explicitly shown. The difference between the above modules in GFLOPs can be seen in Table 3, where the MRPE has a relatively obvious improvement in GFLOPs. On the other hand, the REN significantly improves the accuracy of small-scale objects to the extent that it outperforms the corresponding module of YOLOv9. To validate the effectiveness of our proposed REN in a wide range of object detection methods, we conducted comparison experiments between the REN and the dominant upsampling \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **MRPE** & **REN** & **mAP@50/R** & **mAP@50/R (Edge Rebar)** & **mAP@50/R** \\  & & & & & **(Rebar)** \\ \hline YOLOv9-c & & & 91.4\%/89.6\% & 94.3\%/88.9\% & 98.3\%/98\% \\ YOLOv9-c & ✓ & & 91.6\%/89.9\% & 94.5\%/89.4\% & 98.5\%/98.3\% \\ YOLOv9-c & & ✓ & 91.9\%/89.6\% & 94.3\%/90\% & 98.3\%/98\% \\ YOLOv9-c & ✓ & ✓ & 92.8\%/90.7\% & 95.4\%/90.8\% & 98.9\%/98.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effectiveness of different algorithms on our dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **GFLOPs** \\ \hline YOLOv9 & 237.7 \\ YOLOv9 + SE & 244.8 \\ YOLOv9 + SGE & 245.1 \\ YOLOv9 + MRPE (ours) & 247.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of MRPE and similar methods in GFLOPs. methods in two detection models, HWD and DySample, and we fine-tuned the network structure of HWD and DySample to fit the YOLOv9 model better, as shown in Table 4; by replacing the modules of YOLOv9, the REN obtained a certain degree of improvement in the category of rebar. This is primarily due to the fact that in the REN module, the adaptor expands the low-resolution features in both the horizontal and vertical directions, while the rebar itself exhibits horizontal or vertical orientations. However, the REN has a limited effect on edge rebar. Upon analysis, this is mainly because such targets are too small, and merely performing 2\(\times\) upsampling is insufficient to achieve more accurate results. Similar to the MRPE module, the GFLOPs difference is shown in Table 5. It is worth noting that, compared to DySample and HWD, the REN not only achieves accuracy that is on par with them but also reduces the parameter count by nearly 2.1. This is mainly due to the role of the encoder in the REN module, which reduces the number of original channels and retains the most important channels for feature extraction, thus reducing the computational cost of the model. mAP@50 first calculates the top 50 predictions in the prediction results sorted by confidence and calculates the average precision (AP) among these predictions. These AP values are then averaged to obtain the mAP@50 value, which is used to characterize the quality and stability of the overall detection results. Recall stands for the recall rate, which is the proportion of positive cases that are successfully predicted by the model out of all true positive cases.