## 4 Results and Discussions
### Dataset  
When photographed with the drone-mounted monocular camera, the QR code attached to the floor and product is distorted, due to perspective transformation. Accordingly, the segmentation model should be able to detect the QR code in which distortion due to perspective transformation occurs in the video frame. We generated the train dataset of the segmentation model to detect QR code for which perspective transformation had occurred. The QR code is generated based on the Reed-Solomon code, which has the ability to restore images, even if some areas are missing or damaged. There are four levels of error recovery: L, M, Q, and H. The H with the best recovery performance can recover up to 30% of misses and losses. Warehouse, the inventory management space to use the QR code as the marker, is an environment where the QR code is prone to contamination and damage. Therefore, in this study, the experiment was performed using the QR code of the H level. The QR code generated in the form of square was randomly resized, perspective transformation was applied, and it was rotated randomly within 360\({}^{\circ}\). In order to apply the perspective transformation that can occur in reality, we adjusted the degree of distortion so that the order of the QR code vertices does not change.  
We used the Places365 dataset for the background, which consists of scene categories [11]. The dataset for training the segmentation model was constructed by distorting the QR code and synthesizing them into the background image. In this, two to five QR codes were synthesized in one image. The QR codes attached as the markers for inventory management do not overlap each other. Thus, the QR codes that are synthesized onto the background image do not overlap one another. Furthermore, we generated the mask image representing the QR code area for the training of segmentation model. We synthesized the area of the QR code into white on the black background to generate the mask image. Figure 7 shows the dataset generation process. The size of the generated segmentation  
Figure 6: Results of the position estimation based on the angle between line pairs for the same direction vector error. The red line means the estimated direction vector.  
dataset is 640 \(\times\) 640. The dataset was divided into 4000, 1000, and 1000 pieces of train, validation, and test datasets.  
For performance evaluation of the proposed framework, we generated the video dataset by photographing the ground QR code and inventory QR code in the real environment. Only one ground QR code was attached to the ground, and the 3D position of the center was defined as (0, 0, 0). The 3D positions of inventory QR codes were determined based on the actual distance from the ground QR code. In this study, 1 in 3D space means 1 m. The alignment pattern of the QR code is provided only in version 2 and later QR codes. The proposed framework uses the alignment pattern of the QR code to estimate the drone's 3D position using one ground QR code. As shown in Figure 8, we built the version 3 QR code to a size of 0.2 m, and used it as the ground QR code and inventory QR code. We photographed the QR codes attached to the floor and inventory, and generated the video dataset for 50 inventory QR codes. The frame size of the generated video dataset is 1440 \(\times\) 1440. In addition, the generated video dataset was shot at 30 frames per second.