# Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards
## 3 Experimental Results
### Experimental Setup
For the sake of simplicity, this model contains only one frontend network and one backend network for single-class inspection; the names of layers are defined in Figure 5. More frontend networks and/or backend networks with the same specification can be appended in the model for applications requiring multiple groups with multiple classes. Table 3 shows the specifications of Model 1, Model 2, and Model 3 for five component classes. For meaningful comparisons, as shown in Table 3, all the models have the same dimensions (\(512\times 512\)) for input image \(X\); that is, the original width and height are \(W=H=512\). In addition, they have the same dimensions (\(128\times 128\)) for output images \(Y\) and \(S\). Because \(W=H=512\), we see that the output stride size is \(R=4\) for heatmap generation. We can also observe from Table 3 that Model 1 has the smallest size as compared with Model 2 and Model 3. This is because Model 1 has only a single frontend NN and a single backend NN shared by all the component classes. In contrast, in Model 2, a dedicated backend NN is assigned to each component class. Furthermore, all the component classes are separated into \(P=2\) groups in Model 3. The screws, mounting holes, and capacitors form the first group, and the three-pin and eight-pin chips are in the second group. In Model 3, each group has its own frontend NN. Therefore, Model 3 has the largest size. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Training Process** & **Capacitor** & **Screw** & **Mounting Hole** & **3-Pin Chip** & **8-Pin Chip** \\ \hline Number of Images & 2307 & 2119 & 2839 & 2331 & 1602 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of augmented images for the training of each component class. Figure 8: Examples of different cropping results for the training images. All object targets to be detected on the training images are marked in the examples. Objects in green, red, yellow, blue and purple boxes are mounting holes, capacitors, screws, 3-pin chips and 8-pin chips, \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline Network & \multicolumn{6}{c}{**Functional Network**} & \multicolumn{6}{c}{**RedRed Network**} \\ \hline
**Layer** & **Conv 1** & **Redbook-1** & **Redbook-2** & **Conv 1** & **Conv 1** & **Conv 2** & **Conv 2** & **Conv 3** & **Conv 4** & **Conv 5** & **Conv 6** & **Conv 7** \\ \hline
**Stoke Size** & 2 & 2 & 2 & 2 & 2 & 2 & 1 & 2 & 1 & 1 & 1 & 1 \\
**Kernel Size** & \(7\times 7\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(1\times 1\) & \(3\times 3\) & \(1\times 1\) \\ \hline
**Lepep Number** & \(512\times 512\times 3\) & \(256\times 256\times 32\) & \(128\times 128\times 64\) & \(64\times 64\times 128\) & \(128\times 128\times 128\) & \(256\times 256\times 64\) & \(256\times 256\times 64\) & \(128\times 128\times 128\) & \(128\times 128\times 64\) & \(128\times 128\times 128\) \\ \hline
**Lepep Size** & \(2\times 36\) & \(2\times 36\) & \(118\times 104\) & \(147\times 584\) & \(70\%\) & \(36\%\) & \(36\%\) & \(70\%\) & \(129\) & \(70\%\) & \(28\) \\ \hline
**Network Size** & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: The parameters of the example model containing only one frontend network and one backend network for single-class inspection. The names of layers are defined in Figure 5. The layer size and network size are the number of weights for a layer and a network, respectively.