# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 6 Conclusions  
In this paper, we introduce a multi-class prior-enhanced resolution-extended (PERE) detection framework based on YOLOv9, specifically designed to improve object detection in industrial scenes. To address the challenge of missed detection and false detection, we propose a novel learning strategy leveraging multi-class perceptual prior enhancement (MRPE). This approach allows the detector to integrate prior knowledge from industrial environments, effectively enhancing feature representations and reducing uncertainty in the detection process. Additionally, to improve the detection accuracy of small objects, which are common in industrial inspection tasks, we introduce a resolution-extended network (REN). The REN supplements low-resolution features with additional contextual information, significantly enhancing the detection performance of tiny objects that may otherwise be overlooked. Extensive experimental evaluations validate the effectiveness of our proposed PERE framework, demonstrating superior performance in terms of both accuracy and robustness when compared to existing detection methods. The PERE framework  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & **mAP@50/R (Line)** & **mAP@50/R (Edge Rebar)** \\ \hline YOLOv9 & 57.8\%/49\% & 94.3\%/88.9\% \\ REN s1 & 58\%/48.6\% & 94.1\%/89.7\% \\ REN s2 & 58.6\%/49.6\% & 94.1\%/90.4\% \\ REN s3 & 58.8\%/48.3\% & 94.3\%/90\% \\ \hline \end{tabular}
\end{table}
Table 7: REN internal ablation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Methods** & **MRPE** & **REN** & **mAP@50/R** & **mAP@50/R (Edge Rebar)** & **mAP@50/R (Rebar)** \\ \hline YOLOv5-1 & & & 89.9\%/88.1\% & 92.1\%/88.6\% & 97.6\%/96.2\% \\ YOLOv9-c & & & 91.4\%/89.6\% & 94.3\%/88.9\% & 98.3\%/98\% \\ PERE (ours) & ✓ & ✓ & 92.8\%/90.7\% & 95.4\%/90.8\% & 98.9\%/98.1\% \\ \hline \end{tabular}
\end{table}
Table 8: Effectiveness of different algorithms on our dataset.  
shows significant promise for real-world industrial applications, particularly in scenarios such as laminated panel inspection. Moreover, the methodology is highly adaptable and can be extended to other industrial inspection tasks, including those in assembly line environments. Our results suggest that the core principles of PERE, including the integration of perceptual priors and resolution extension, could provide substantial improvements in a wide range of industrial contexts. In future work, we plan to explore the scalability of the PERE framework in more complex industrial scenarios, such as multi-object detection in dynamic environments and the application of the model in different production lines. Additionally, we will investigate the potential for further optimization and adaptation of the proposed method to handle a broader range of industrial inspection challenges.  
Conceptualization, H.W. and X.X.; methodology, H.W. and Y.W.; software, H.W. and Y.W.; validation, H.W., Y.W. and J.L.; formal analysis, H.W. and Y.W.; investigation, H.W.; resources, H.W.; data curation, H.W. and J.L.; writing--original draft preparation, H.W. and Y.W.; writing--review and editing, H.W., Y.W. and J.L.; visualization, H.W.; supervision, J.L. and X.X.; project administration, H.W.; funding acquisition, X.X. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.