## 4 Results and Discussions
### Segmentation-Based QR Code Decoding Test  
We used the generated dataset to train the segmentation model to detect the QR code area in the video frame. The QR code decoding tests were performed by training  
Figure 8: Example of test videos for the verification of the proposed model. Figure 7: Image synthesis for dataset generation. UNet, UNet++, PSPNet, DeepLabV3, and DeepLabV3+ to select the optimal segmentation model. ResNet101 [55] and Xception [56] were used as the encoder for the segmentation model. A total of eight segmentation models were generated using five architectures and two encoders. In every experiment, we employ the Adam optimizer [57]. The generated segmentation models were trained for 50 epochs. Initial learning rate was used (\(10^{-4}\)). The learning rate was adjusted to \(10^{-5}\) after 25 epochs. Input batches are randomly sampled during training. The batch size is 8. The Dice Loss function [58] was adopted for segmentation model training. Intersection over Union (IoU) was used as a metric to evaluate the training results of the segmentation models. IoU is a measure of the overlap between the area predicted by segmentation model and ground truth. The segmentation model aims to detect the QR code area in the video frame, correct the perspective transformation, and then perform decoding to obtain the information of the QR code. In other words, the better the decoding results of the detected QR code areas, the more suitable the segmentation model is for our framework. Therefore, the decoding success rate of the QR code area detected from the segmentation model selected by the IoU was used as an evaluation index of the segmentation models. We used 0.5 as a threshold of the metric. The segmentation model was trained on the generated train dataset. Then, the decoding test was performed using the segmentation model with the highest IoU score on the generated validation dataset. The decoding test was performed on the generated test dataset of the segmentation image dataset. The test dataset contains 1931 QR codes that can be decoded through distortion correction. Table 1 shows that the segmentation model combining Xception and UNet++ achieved the highest IoU score and decoding performance. Figure 9 shows the results for the input image of the trained segmentation model. As a result of the segmentation model experiment, the model combining Xception and UNet++ was the most suitable model for our framework. In subsequent experiments, the segmentation model was used for QR code area detection in our framework. We performed the segmentation and the QR code decoding of the video dataset using the trained segmentation model. As shown in Figure 10, we confirm that the trained \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{2}{c}{**Model**} & & \\ \hline
**Decoder** & **Encoder** & **IoU** & **Decoding Rate** \\ \hline Decoder & Encoder & IoU & Decoding Rate \\ U-Net & ResNet101 & 0.9869 & 98.71\% \\ U-Net++ & ResNet101 & 0.9894 & 98.71\% \\ PSPNet & ResNet101 & 0.9798 & 85.91\% \\ DeepLabv3 & ResNet101 & 0.9815 & 91.77\% \\ DeepLabv3+ & ResNet101 & 0.9736 & 97.82\% \\ U-Net & Xception & 0.9890 & 98.81\% \\ U-Net++ & Xception & 0.9898 & 99.07\% \\ PSPNet & Xception & 0.9544 & 75.87\% \\ \hline \hline \end{tabular}  
* IoU = overlapping region/combined region. Decoding Rate = number of the QR code decoded/number of all QR codes.  
\end{table} 
Table 1: Results of the QR code recognition based on the segmentation model. Figure 9: Results of the segmentation model (**right**), input image (**left**), and ground truth (**center**). segmentation model performs well on the video dataset. We proposed the method of decoding the QR code with distortion using segmentation-model-based area detection of the QR code and the computer vision algorithm. However, the QR code decoding is difficult if the segmentation is not performed well due to various noises. Therefore, if the performance of the segmentation model is improved, the decoding performance of the QR code may also be improved in the real-world.