## 5 Conclusions  
This paper presents PCES-YOLO, a high-precision PCB detection model based on YOLOv11n. A 3 \(\times\) 3 convolution is added before the RFE module to form the PRFE module, which reduces high-frequency interference and prevents excessive dilution of detailed features by large receptive fields. The PRFE module is then integrated with C3k2 to create the C3k2-PRFE feature extraction module. Furthermore, the ConvNeXtBlock module replaces C3k2 in the P4 layer. These modifications enhance the model's ability to capture a wide range of semantic information, deepen its understanding of input data, and effectively reduce small target feature loss. The innovative EFAN achieves two main goals: reducing the parameter count while effectively integrating shallow detail features with deep semantic features, ensuring the retention of critical defect information across different feature levels. During training, PCES-YOLO employs the Shape-IoU loss function, which emphasizes the geometric attributes of bounding boxes during loss computation, thereby improving regression stability. The experimental results indicate that the proposed model outperforms mainstream detection models in comparison, with improvements of 2.5% in precision, 4.9% in recall, 3.6% in mAP50, and 15.2% in mAP50-95 over YOLOv11n, and effectively addresses the issues of small target feature loss and severe background interference. For industrial deployment, future work will focus on model lightweighting to reduce computational costs without sacrificing accuracy, enabling embedded device deployment. Further optimization will involve real production line testing to enhance applicability in PCB manufacturing.  
Conceptualization, H.Y. and H.C.; methodology, H.Y.; software, H.Y.; validation, H.Y., J.D. and C.W.; formal analysis, H.C., Z.L. and J.D.; investigation, H.Y. and H.C.; resources, H.C. and C.W.; data curation, H.Y.; writing--original draft preparation, H.Y. and J.D.; writing--review and editing, Z.L. and H.C.; visualization, H.Y.; supervision, H.C.; project administration, H.Y.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shandong Provincial Natural Science Foundation (No. ZR202103010716), the National Natural Science Foundation of China (No. 62473238), the Shandong Province Major Science and Technology Innovation Project (No. 2022 CXGC020204), and the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China (No. 2023 KG304).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **FPS** & **GPU Memory Usage/\%** \\ \hline YOLOv11n & 139 & 18 \\ YOLOv11s & 115 & 32 \\ PCES-YOLO & 133 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative experimental results for frame rate and GPU memory usage.  
**Acknowledgments:** The authors gratefully acknowledge the reviewers' constructive suggestions and insightful comments that have helped strengthen this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.