# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 4 Experimental Results and Discussion
### Comparative Experiments
#### 4.4.2 Model Comparison Experiment
Also, the detection results for this aluminum profile dataset using the method proposed in this paper improved _PR_, _TPR_, _TNR_, _F1_, and _AUC_ by 2.2%, 5.6%, 8.9%, 3.9%, and 5.7%, respectively, when compared with the detection results on the DPAE method. Experiments have shown that the model proposed in this paper can effectively detect whether there are defects on the surface of aluminum profiles. The results using our model are compared with the MA-YOLO method proposed in the literature [42], and some other mainstream supervised surface-defect detection methods mentioned in that literature, as shown in Table 4.  
As can be seen from Table 4, the _PR_, _RC_, and _F1_ values of our proposed method on this dataset are significantly higher than those of SSD300, Faster R-CNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv3, YOLOv7, DETR, and MA-YOLO, and the _PR_, _RC_, and _F1_ values are at least improved by 1.6%, 5.2%, and 10.3%, respectively. Our experiments have shown that the model in this paper can effectively detect whether there are defects on the surface of aluminum profiles. Table 5 shows an example of the identification process for some of the samples, where the first two rows are correctly identified samples and the last two rows are incorrectly identified samples, the penultimate column is the actual output and the last column is the desired output. As can be seen from Table 5, due to lighting, shooting angle, and other issues resulting in large differences in the background of aluminum profiles in this dataset, the main part of the aluminum profiles is affected by varying degrees of exposure, uneven illumination, strong reflections, and background noise, etc. However, the model proposed in this paper can effectively remove the interference of the complex textures on the surfaces of the aluminum profiles while retaining their defective features, and can effectively identify defective samples. The main reason for the identification error of the third row of samples \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & _PR_ & _RC_ & _F1_ \\ \hline SSD300 & 0.958 & 0.511 & 0.672 \\ Faster R-CNN & 0.541 & 0.879 & 0.658 \\ YOLOv3 & 0.901 & 0.787 & 0.826 \\ YOLOv4 & 0.928 & 0.599 & 0.671 \\ YOLOv5s & 0.884 & 0.792 & 0.827 \\ YOLOv\_s & 0.875 & 0.829 & 0.847 \\ YOLOv6s & 0.769 & 0.725 & 0.739 \\ YOLOv7 & 0.878 & 0.554 & 0.639 \\ DETR & 0.711 & 0.837 & 0.778 \\ MA-YOLO & 0.908 & 0.811 & 0.849 \\ Ours & 0.974 & 0.931 & 0.952 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results with supervised deep learning models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _PR_ & _TPR_ & _TNR_ & _F1_ & _AUC_ \\ \hline AE(L2) & 0.78 & 0.74 & 0.45 & 0.76 & 0.68 \\ AE(SSIM) & 0.83 & 0.64 & 0.66 & 0.73 & 0.67 \\ VAE & 0.79 & 0.64 & 0.54 & 0.71 & 0.63 \\ AnoGAN & 0.69 & 0.68 & 0.58 & 0.63 & 0.64 \\ GANomaly & 0.79 & 0.74 & 0.49 & 0.76 & 0.72 \\ DPAE & 0.96 & 0.92 & 0.89 & 0.94 & 0.92 \\ Ours & 0.97 & 0.93 & 0.98 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model comparison results. is that the lack of prior features of the sample is too small, and in the process of feature optimization, the defective features and texture are optimized together, which affects the detection results. For the last row of samples from the acquisition process using the shooting angle, the image has part of the aluminum profile side information; under the influence of the lighting effect, the side information color presentation and the rest of the main part of the difference, and in the process of data normalization is retained, the gloss, color difference affects the final results of the detection.