# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 5 Experimental Results Validation Analysis
### Multi-Model Comparison Experiments  
The conduct of comparative experiments using multiple models is a common and important step in the process of model evaluation. The objective of comparative experiments is to assess the performance of different models comprehensively, identify the most suitable model for a particular task, and ensure the reliability and robustness of the model under various conditions. By conducting a systematic comparison of the performance of different  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP/\%** & **Params/10\({}^{\text{\textregistered}}\)** & **GFLOPs** & **FPS** \\ \hline YOLOv8 & 61.2 & 3.01 & 8.1 & 197 \\ YOLO-RRL & 62.3 & 2.78 & 7.4 & 238 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of APSPC on the dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP/\%** & **Params/10\({}^{\text{\textregistered}}\)** & **GFLOPs** & **FPS** \\ \hline YOLOv8 & 73.1 & 3.01 & 8.1 & 157 \\ YOLO-RRL & 73.9 & 2.70 & 7.4 & 217 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of NEU-DET on the dataset.  
models, it is possible to make more informed choices, thereby ensuring that the models will operate efficiently and reliably in real-world applications.  
The experiment employs a range of classical mainstream detection models, including not only the single-stage detection model Fast R-CNN [28] but also the two-stage detection models of the YOLO series and SSD. To ensure the validity and authenticity of the comparison experiments, the dataset used is identical to that in Section 3.2, and the experimental equipment and environmental settings are the same as those in Section 3.1. Table 5 presents the experimental results of different models on the PCB dataset.  
Table 5 lists the experimental performance of different detection models on the PCB surface defect dataset, comparing the performance of each model across several metrics. The mAP of the RT-DETR model [29] is the lowest among all experiments, being 9.9% lower than that of YOLOv8-RRL. The Fast R-CNN model has the highest number of parameters and computational cost in all experiments, with 134.32 \(\times\) 10\({}^{6}\) parameters and a computation amount 362.8 higher than that of YOLOv8-RRL. Additionally, the FPS of the Fast R-CNN model is the lowest among all experiments, being 86% lower compared with YOLOv8-RRL.  
Table 6 presents a comparison of YOLO-RRL with previous works in the field. YOLO-MBBi [30], developed by [PERSON] et al., is a PCB defect detection model based on the YOLOv5s network. The improvements include the integration of MBConv modules, CBAM attention mechanisms, BiFPN, and depthwise convolutions, along with replacing the CIoU loss function with the SIoU loss function. [PERSON] et al. proposed a new model, LFF-YOLO [31], demonstrating highly competitive detection accuracy for PCB defects. From the observations in Table 6, YOLO-RRL outperforms YOLO-MBBi with a 0.1% improvement in mAP, a reduction of 5.4 GFLOPs, and an increase of 157.1 FPS. Compared with LFF-YOLO, YOLO-RRL shows a 2.0% enhancement in mAP. These results indicate that YOLO-RRL exhibits superior overall performance.  
In summary, the YOLO-RRL model performs well in PCB defect detection, having the highest accuracy and maintaining low computational complexity and high frame rate, making it a good balance between accuracy and efficiency and very suitable for application in real industrial environments. The other models have their advantages and disadvantages and can be selected according to specific application scenarios and needs.