# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 5 Conclusions  
The unsupervised surface-defect detection method for aluminum profiles focusing on feature reconstruction contains three parts: aluminum profile image preprocessing, essential feature learning and surface-defect detection. The adaptive boundary extraction method of the preprocessing process can accurately determine the main part of the aluminum profile under the background of different colors and complex lighting, can reduce redundant data, and can lay the foundation for improving the accuracy of the detection method. The feature-optimization module is integrated into the MAE model, which effectively eliminates the interference of the complex texture randomly distributed on the surface of aluminum profiles, improves the model's focus on the essential features of aluminum profiles, weakens the influence of irrelevant information on the model, and effectively extracts the surface features of the good aluminum profiles. The method also adds a fixed local masking strategy, which masks the defective information on the surface of the aluminum profile as much as possible during the detection process, and it is able to obtain its global features from the unmasked area, which ultimately achieves a better repair effect. The experimental results show that its detection accuracy reaches 97.7%, compared with the current common methods for detecting defects on the surface of aluminum profiles. The training process for this method does not require defect samples or a large amount of data labeled in advance,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Blocks** & **8 \(\times\) 8** & **16 \(\times\) 16** & **32 \(\times\) 32** \\ \hline _PR_ & 0.581 & 0.974 & 0.738 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of detection results within different blocking scenarios.  
Figure 20: Comparison of model outputs for different blocking scenarios (some examples).  
which reduces the labor cost. At the same time, the method is not subject to the limitations of the types of defect samples, giving higher accuracy, and the equipment required for the model training meets the cost requirements of industrial production. However, the proposed method still has challenges in distinguishing small and low-contrast defects, so future research will continue to explore more efficient feature-optimization methods.  
Conceptualization, Y.Z. and S.T.; Methodology, Y.Z. and Z.J.; software, Y.Z. and H.L.; validation, Z.J.; investigation, Z.J. and J.Y.; writing--original draft preparation, Y.Z. and J.L.; writing--review and editing, H.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2018 YFC0808300), Shaanxi Science and Technology Plan Key Industry Innovation Chain (Group)--Project in Industrial Field (2020 ZDLGY15-07), Xi'an Science and Technology Program (23 KGDW0032-2022).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The dataset used in this study was downloaded at: [[https://tianchi.aliyun.com/competition/entrance/231682/information](https://tianchi.aliyun.com/competition/entrance/231682/information)]([https://tianchi.aliyun.com/competition/entrance/231682/information](https://tianchi.aliyun.com/competition/entrance/231682/information)).  
The authors declare no conflict of interest.