# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 3 Experiments
### Contrast Experiments
#### 3.3.2 Real Collected Datasets  
The two kinds of defect datasets collected contains defective and defect-free images, so they can be regarded as multi-classification or binary classification tasks. (A) Localization and Classification Results of the Diode Glass Bulb Surface Defect Dataset: For the diode glass bulb surface defect dataset, the ratio of the training set and testing set images is 7:3. Some experimental localization results of defect detection on this dataset by the proposed method are shown in Figure 10.  
There is no significant texture difference around different defect types in the diode glass bulb surface defect dataset, and shell wall damage is a typical shape defect. However, it can be found that the proposed method can accurately extract the key pixel regions that discriminate each type of defect, which can not only explain the reason why it can achieve a higher precision than other methods, but also obtain the better effect of localization. The comparative experiments on this dataset are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Average Precision** \\ \hline GLCM + MLP & 98.61\% \\ gcForest & 61.56\% \\ BCNN & 98.56\% \\ BYEC & 96.30\% \\ Song [35] & 98.60\% \\ Ren [18] & 99.21\% \\ Ours & 99.44\% \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of results on NEU surface defect dataset. Figure 10: Examples of localization on the diode glass bulb surface defect dataset. From top to bottom are the original image, the combination of the original image and the heat map, and the location results of these defects. The Ground-Truth of the defect is marked with the red bounding boxes, while the localization results of the proposed method is marked with the blue bounding boxes. (**a**) break; (**b**) shell wall damage; (**c**) stain. It can be seen from Table 3 that even in the work-piece surface defect detection task with few texture features, the proposed method has an advantage in detection accuracy compared with other algorithms. (B) Localization and Classification Results of the Fluorescent Magnetic Powder Surface Defect Dataset: For this fluorescent magnetic powder surface defect dataset, the ratio of the training set and the testing set images is 7:3. The experimental localization results of the proposed method on this dataset are shown in Figure 11.  
When the ultraviolet light is irradiated on the smooth iron work-piece, the surface of the magnetized work-piece will reflect the violet light emitted by the ultraviolet light due to the principle of light reflection. This phenomenon is particularly prominent on the cylindrical work-piece. Therefore, the defect image of the fluorescent magnetic powder obtained in the experiment has a bright purple reflective area in the center of the work-piece, which will cause a great interference to the detection of any defects. In the experiment, the original image is zoomed into a size of \(448\times 448\), with no pre-processing having been performed on the images except for normalization, and then the image is sent to the network for training and testing. As shown in Figure 11, it can be seen that the network can  
Figure 11: Localization results of the proposed method on the fluorescent magnetic powder surface defect dataset. From top to bottom are the original image, the combination of the original image and the heat map, and the location results of these defects. The Ground-Truth of the defect is marked with the red bounding boxes, and the localization result of the proposed method is marked with the blue bounding boxes. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Average Precision** \\ \hline GLCM + MLP & 91.32\% \\ gcForest & 85.25\% \\ BCNN & 91.80\% \\ Ours & 99.87\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of results on the diode glass bulb surface defect dataset. effectively eliminate interference in the reflective area and extract the defective area.