# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 5 Experimental Results Validation Analysis  
### Ablation Experiments  
The principal objective of the ablation experiments presented in this paper is to evaluate the influence of specific components or features of the model on its overall performance by modifying them. Consequently, it is possible to ascertain which components or features contribute the most to the model's performance, to validate various assumptions in the model design, to ensure that the design of each part is reasonable and necessary, and by analyzing different configuration options, it is possible to identify the optimal combination of modules to achieve a balance between performance and efficiency. The original YOLOv8 model is identified as the baseline model, upon which different components are improved, and the training results of the model after each modification are recorded. The detailed experimental results of the different modified models are presented in Table 2. The following section presents a series of distinct modification scenarios. 1. The Backbone network downsampling module is replaced by the Robust Feature Downsampling (RFD) module, which enhances the model's capacity to capture local and global information by optimizing the sensory field design, thereby enabling the model to extract more detailed information. 2. The neck is partially replaced by a RepGFPN feature fusion network, which enables the network to capture both high-level semantic information and low-level spatial details by fusing feature maps from different scales, thus improving the accuracy of target detection. 3. The up-sampling module of the neck part of the original model is replaced by the DySample module. This ensures the high accuracy and applicability of the up-sampling, improves the inference speed of the model, and enables the model to process high-resolution images quickly. 4. The replacement of the head component of the original model with a lightweight asymmetric detection head (LADH-Head) is proposed as a means of reducing the number of parameters and computational cost of the model. 5. The up-sampling module of the RepGFPN network will be enhanced based on scheme b, utilizing the DySample module, which enables the network neck to concentrate on a greater quantity of feature information. 6. The original model is to be enhanced simultaneously with the implementation of schemes A, D, and E to create the YOLO-RRL model. From the table, it can be seen that the ablation experiments evaluate different model configuration schemes (Plan A-F). When compared with the baseline model, it can be seen that the proposed modifications achieve different degrees of improvement. The baseline model serves as a reference point, exhibiting satisfactory accuracy and average precision, although it is characterized by a higher number of parameters and computational complexity, as well as a lower frame rate. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Plan**} & \multirow{2}{*}{**RFD**} & \multirow{2}{*}{**RepGFPN**} & \multirow{2}{*}{**DySample**} & \multirow{2}{*}{**LADH**} & \multicolumn{6}{c}{**AP/\%**} & \multirow{2}{*}{**P/\%**} \\ \cline{5-16}  & & & & & & **Mh** & & **Mb** & & **Ch** & **Sp** & **Sc** & \\ \hline Baseline & — & — & — & — & — & 97.2 & 93.9 & 96.3 & 93.2 & 91.9 & 91.9 & 94.1 & 93.0 & 3.01 & 8.1 & 184 \\ A & \(\surd\) & — & — & — & — & 98.8 & 94.7 & 94.0 & 97.2 & 95.8 & 96.5 & 96.1 & 94.8 & 3.03 & 9.6 & 159 \\ B & — & \(\surd\) & — & — & — & 98.9 & 93.8 & 94.5 & 97.4 & 96.2 & 95.6 & 96.1 & 94.2 & 3.26 & 8.3 & 213 \\ C & — & — & — & — & — & 97.4 & 94.3 & 96.0 & 97.4 & 95.0 & 94.5 & 95.7 & 94.2 & 3.01 & 8.1 & 222 \\ D & — & — & — & — & 97.8 & 93.9 & 94.4 & 96.6 & 95.6 & 94.5 & 95.5 & 93.8 & 2.38 & 5.7 & 232 \\ E & — & \(\surd\) & \(\surd\) & \(\surd\) & & 98.4 & 95.4 & 95.7 & 97.3 & 96.9 & 95.6 & 96.6 & 94.5 & 3.26 & 8.3 & 227 \\ F & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 99.2 & 95.6 & 93.7 & 97.3 & 95.8 & 95.4 & 96.2 & 95.2 & 2.70 & 7.4 & 206 \\ \hline \hline \end{tabular}
\end{table}