# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 6 Conclusions  
In the field of printed circuit board surface defect detection, the main problems of end-device computational power and target features that are not easy to detect usually  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mAP/\%** & **GFLOPs** & **FPS** \\ \hline YOLO-MBBi & 95.3 & 12.8 & 48.9 \\ LFF-YOLO & 93.31 & — & — \\ YOLO-RRL & 95.4 & 7.4 & 206 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results of YOLO-RRL, YOLO-MBBi, and LFF-YOLO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP/\%** & **Params/10\({}^{6}\)** & **GFLOPs** & **FPS** \\ \hline Fast R-CNN & 92.8 & 137.10 & 370.2 & 28 \\ SSD & 87.3 & 26.29 & 62.7 & 62 \\ YOLOv5 & 92.6 & 2.51 & 7.1 & 192 \\ YOLOv6 & 90.9 & 4.23 & 11.8 & 188 \\ YOLOv7 & 91.1 & 6.02 & 13.1 & 105 \\ YOLOv8 & 93.0 & 3.01 & 8.1 & 183 \\ RT-DETR & 85.5 & 28.5 & 100.6 & 89 \\ YOLO-RRL & 95.4 & 2.78 & 7.4 & 206 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative Experimental Results of Different Models on PCB Defect Dataset.  
occur. To solve these problems, we make a large number of advanced improvements in the existing YOLOv8 detection model and propose a lightweight printed circuit board surface defect detection model, YOLO-RRL. After a large number of experiments, it is shown that the YOLO-RRL model demonstrates the excellent characteristics of high accuracy, low complexity, moderate computational requirements, and high processing speed for the task of detecting defects in the PCB surface defect dataset. Experimental results on NEU-DET and APSPC datasets demonstrate good robustness. In comparison with different models, YOLO-RRL has a very good competitiveness, and the practical significance of YOLO-RRL is reflected in its ability to significantly improve production quality and reduce production cost, as well as its wide applicability and real-time application capability. Therefore, the application of the YOLO-RRL model in industrial production is very promising and has important practical value. Despite the introduction of advanced methods, including RFD, RepGFPN, DySample, and LADH, certain defects remained undetected. To illustrate, the spurious copper category demonstrated consistently low AP scores across multiple schemes, indicating that the model encounters difficulties in addressing this particular type of defect. This issue may originate from the paucity of diversity in the training datasets for this category, which impairs the model's capacity for generalization. To overcome these obstacles, future research should prioritize augmenting dataset diversity, particularly for underrepresented categories such as spurious copper. The incorporation of sophisticated data enhancement techniques could enhance the diversity of the training samples and bolster the model's generalization proficiency.  
Writing--review and editing T.Z., J.Z. and P.P.; Supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded and supported by the National Natural Science Foundation of China [grant numbers 52075348, 52175107, 52275119; National Key Research and Development Program of China [Grant numbers 2023 YFB3408502]; Natural Science Foundation of Liaoning Province [grant numbers 2022-MS-280]; Shenyang Outstanding Young and Middle-aged Science and Technology Talents Project, [Grant numbers RC230739]; Basic Scientific Research Project of Liaoning Provincial Department of Education [Grant numbers] YTMS20231568].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.