# Infrared Fault Classification Based on the Siamese Network
## 5 Conclusions  
To address the issue of a limited dataset of photovoltaic failure images, an ACA-FFM Siamese convolutional neural network model was proposed. The model performs feature extraction followed by similarity logical regression. The key objective of this technology is  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Cell** & **Cracking** & **Diode-Multi** & **Hot-Spot-Multi** & **No Anomaly** & **Offline-Module** \\ \hline ACCR & 76.6 & 86.7 & 72.8 & 98.2 & 91.3 & 93.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class-based experimental results.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Params** & **Acc** & **Over Fitting** & **Predict Time (s)** \\ \hline Mobilenetv2 & 1.9 M & 70.10\% & No & 0.12 \\ EfficientNetV2\_s & 22.3 M & 72.90\% & Yes & 0.17 \\ Resnet18 & 11.1 M & 78.80\% & Yes & 0.15 \\ Resnet34 & 21.2 M & 80.50\% & Yes & 0.17 \\ CNN-AF & 0.9 M & 83.90\% & No & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different backbone networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN** & **ACA** & **FFM** & **ACC** \\ \hline ✓ & & & 67.70\% \\ ✓ & ✓ & & 75.30\% \\ ✓ & ✓ & ✓ & 79.30\% \\ ✓ & ✓ & ✓ & 83.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.  
to reduce the dependence on large-scale datasets. Firstly, a novel feature extraction method based on adaptive coordinate attention (ACA) is proposed. This method dynamically adjusts the scope of module attention based on input images, thereby enhancing the ability of multi-scale feature extraction. Then, a new feature fusion module (FFM) is designed to enhance feature learning ability and improve the accuracy of fault detection. The FFM utilizes ACA to dynamically adjust the attention scope, allowing for more accurate and robust feature extraction. This technology has the potential to significantly improve the accuracy and efficiency of photovoltaic failure image analysis, and has the potential to be applied in a wide range of photovoltaic applications.  
However, although the method achieved improved results, there are still classification errors for similar solar power failure images. Further research will focus on image classification for similar images to improve classification among similar samples.  
Conceptualization, X.W., X.L., Y.W. and B.J.; methodology, X.W. and L.Z.; software, Y.W. and L.Z.; validation, Y.W. and X.L.; formal analysis, X.W. and B.J.; investigation, B.J., Y.W. and Q.B.; resources, Y.W. and Q.B.; data curation, Y.W. and L.Z.; writing--draft, L.Z.; writing, X.W. and L.Z.; visualization, X.L.; supervision, Q.B.; project administration, Q.B. and X.W.; funding acquisition, Q.B., Y.W. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Research and Development Projects in Zhejiang Province grant number No. 2021C03151 and Zhejiang Provincial Public Welfare Research Program grant number No. LGG21G010001.  
Not applicable.  
The authors thank Shangyu XinHeCheng Chemical Co., Ltd. for providing the experimental data in this paper.  
The authors declare no conflict of interest.