## 3 Experiments
### Dataset  
In this work, the visible-light image dataset of the arc magnet appearance comprises two parts: the basic and the expanded data. The former is derived from a public dataset named 'Magnetic-Tile' [39], which is collected by the Chinese Academy of Sciences. Its images cover defect-free arc magnets and those corresponding to five types of surface de  
Figure 3: The experimental rig designed to identify the surface defects on arc magnets: (**a**) the three-dimensional diagram; (**b**) partial actual details of the individual identification of each arc magnet; (**c**) partial actual details of the simultaneous identification of multiple arc magnets.  
fects, including blowhole, crack, fray, break, and uneven. Each image involves one or more surface defects of the same type. There are a total of 1394 surface defects in such a dataset, but only 442 images related to the five types of surface defects. Regardless of the number of images or the number of defect types, the ability of such a dataset to represent sufficient data on arc magnet surface defects is severely limited. Therefore, using the designed experimental rig and prepared arc magnet samples, we supplemented the number of images for the original five types in this dataset and appended three newly common defect types (stained, unfilled, scratched) and their corresponding image quantities, resulting in the expanded data. By adding 1235 surface defects, the total amount of raw data used in this study reached 2629, where the added number of surface defects per type was at least 200 or more. In addition, data augmentation based on image enhancement methods, comprising enhancement and reduction of brightness, horizontal mirroring, vertical flipping, and random rotation, was carried out to further expand the amount of image data to form relatively sufficient data for training, validating, and testing our proposed model [40].  
As a result of data augmentation, our dataset ultimately contained 18,770 image-based data points for both defect-free and defective arc magnets. The images corresponding to defects covered eight types of common surface defects, and the number of images for each type was close to the same. Compared with other studies for arc magnets, the dataset in our work not only embraced a wider range of defect types but also had a more abundant amount of data. Figure 4a shows the appearance of the selected eight types of surface defects and defect-free arc magnets, and Figure 4b is an example for the data augmentation of an image. The details of our dataset are shown in Table 1. Each type of image in the dataset was divided into three groups according to an approximate ratio of 8:1:1 [41]; these groups were utilized as the training set, validating set, and testing set together with the corresponding groups of other types.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{8}{c}{**Basic Data**} & \multicolumn{8}{c}{**Expanded Data**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-11}  & **Free** & **Blowhole** & **Break** & **Crack** & **Fray** & **Uneven** & **Stained** & **Unfilled** & **Scratched** \\ \hline Original & 952 & 115 & 119 & 68 & 37 & 103 & 0 & 0 & 0 & 1394 \\ Added & 0 & 90 & 111 & 149 & 181 & 97 & 207 & 200 & 200 & 1235 \\ Subtotal & 952 & 205 & 230 & 217 & 218 & 200 & 207 & 200 & 200 & 2629 \\ Augmented & 2000 & 2050 & 2300 & 2170 & 2180 & 2000 & 2070 & 2000 & 2000 & 18,770 \\ Training set & 1600 & 1646 & 1835 & 1738 & 1737 & 1600 & 1652 & 1600 & 1600 & 15,008 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical details of the dataset.  
Figure 4: The appearance of experimental samples and the data augmentation of their images: (**a**) the appearance of experimental samples; (**b**) the data augmentation.