## 3 Materials and Methods
### Data Acquisition  
Table 1 provides a comprehensive overview of the frequency of defect classes observed in each batch of image acquisitions. Moreover, the number of ceramic plates examined in each batch is also highlighted.  
Batches 1, 2, 3, and 4 were specifically utilized for training and validation in this study for the development of the models, comprising the first set of images (set 1). Batches 5 and 6 were acquired later and reserved for testing purposes, forming the second set of images (set 2). Visual representations of each defect class are presented in Figure 3. An analysis of the defect class distribution highlights notable variations. Defects such as Defect-10 and Defect-12 exhibit a higher occurrence rate, observed across multiple batches. In contrast, Defects 2 and 13 have a minimal presence within the dataset. Defects 14  
Figure 2: Experimental setup developed for image acquisition.  
and 15 exclusively appear in plates from the last two batches, indicating a more recent occurrence. The observed distribution indicates a significant class imbalance among the defect categories, with certain classes being more prevalent, while others are relatively rare.  
After the data acquisition, images were annotated using an internal tool for annotation that was previously developed and tested with end-users [16]. The development of this  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Class**} & \multicolumn{5}{c}{**Frequency**} & \multirow{2}{*}{**Batch 1**} & \multirow{2}{*}{**Batch 2**} & \multirow{2}{*}{**Batch 3**} & \multirow{2}{*}{**Batch 4**} & \multirow{2}{*}{**Batch 5**} & \multirow{2}{*}{**Batch 6**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \multicolumn{1}{c}{Number of images} & 112 & 136 & 262 & 200 & 148 & 80 & 938 \\ \hline Defect-1 & Blister & 2 & 12 & - & - & - & - & 14 \\ Defect-2 & Glaze roll & 4 & - & - & - & - & 1 & 5 \\ Defect-3 & Lack of glass & 13 & 7 & 11 & 3 & 1 & - & 35 \\ Defect-4 & Broken foot-ring & 6 & 6 & - & - & - & 1 & 13 \\ Defect-5 & Chipped & 6 & 4 & - & - & 3 & - & 13 \\ Defect-6 & Chipped\&glazed & 8 & 4 & - & - & 2 & 3 & 17 \\ Defect-7 & Finishing dirt & 10 & 1 & 2 & 3 & 19 & - & 35 \\ Defect-8 & Oven dirt & 17 & 7 & 1 & - & 4 & - & 29 \\ Defect-9 & Bad finish & - & - & 2 & 3 & 31 & 2 & 38 \\ Defect-10 & Dots & 93 & 77 & 167 & 84 & 71 & 34 & 526 \\ Defect-11 & Pore & 13 & 38 & 25 & 5 & 36 & 16 & 133 \\ Defect-12 & Cracked & 44 & 197 & 19 & 2 & 17 & 45 & 324 \\ Defect-13 & Glass dirt & - & - & 1 & - & 1 & - & 2 \\ Defect-14 & Air in the paste & - & - & - & - & 23 & 1 & 24 \\ Defect-15 & Bad retouch & - & - & - & - & 19 & - & 19 \\ \hline Total number of defects & 216 & 353 & 228 & 100 & 227 & 103 & 1227 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.  
Figure 3: Examples for each defect class, with variable shapes and dimensions. (**a**) Defect-1: Blister; (**b**) Defect-2: Glaze roll; (**c**) Defect-3: Lack of glass; (**d**) Defect-4: Broken foot-ring; (**e**) Defect-5: Chipped; (**f**) Defect-6: Chipped&glazed; (**g**) Defect-7: Finishing dirt; (**h**) Defect-8: Oven dirt; (**i**) Defect-9: Bad finish; (**j**) Defect-10: Dots; (**k**) Defect-11: Pore; (**l**) Defect-12: Cracked; (**m**) Defect-13: Glass dirt; (**n**) Defect-14: Air in the paste; (**o**) Defect-15: Bad retouch.  
tool included iterative usability tests to improve the interface usability, collect insights about defect characterization, and establish design requirements for enhancing the system.  
In Figure 4, it is possible to find two different pieces of tableware (a flat plate--Figure 4d and a cereal bowl--Figure 4b) belonging to batch 3 of data. In these images, the defects annotated by specialists through the developed tool are highlighted in different colors. The annotations are converted to the bounding box format for training of the defect detection module (presented in Section 3.5) The boxes are also used for cropping the regions of interest for defect classification (Section 3.6).