## 1 Introduction
The segmentation model [10] was trained to detect the area of an object in an image. Then, the QR code area was detected from the video frame. The dataset for segmentation model learning was generated by synthesizing the QR code with size, rotation, and perspective distortion in the background image [11]. The generated dataset was used to fine-tune the segmentation model pre-trained with ImageNet [12]. Decoding is performed after detecting the QR code area in the video frame with the fine-tuned segmentation model and correcting the QR code distortion. In the case of ground QR codes as a result of decoding, the drone's 3D position is estimated using the 3D positional information stored in the QR code. Furthermore, the 2D coordinates of the QR code is detected from the image. An equation of a straight line in 3D space passing through the inventory is derived from a drone's position at the time of frame shooting estimated through the ground QR code and the 2D coordinates of the inventory QR code detected in the image. After deriving equations of the straight line from all the video frame, frames in which the estimated drone position were determined to be outliers are detected. Then, the outlier frames are removed to minimize errors in 3D inventory position estimation. The inventory 3D position was estimated with pairs of straight lines derived from the video frame other than the outliers. Finally, the 3D position estimation result of the inventory is corrected using the angle between the straight pairs. The proposed framework consists of deep-learning-based QR code recognition, drone 3D position estimation, outlier frame removal, inventory 3D position estimation, and error correction. Deep-learning-based QR code recognition shows that a high level of marker recognition is possible by combining the segmentation model and the computer vision algorithm. Outlier frame removal and error correction shows the relationship between the angles between epipolar lines and the estimated inventory 3 Dposition. It also shows that it is possible to correct the estimated 3D position error using this relationship. The contributions of our study can be summarized as follows:  
* 3D position estimation of inventory based on the deep learning model using a drone. We propose the 3D position estimation framework for the inventory based on the deep learning model using the QR code and drone-mounted monocular camera. The proposed framework estimates the 3D position of the inventory from the video frame using the deep learning model trained from our generated dataset. * Estimated inventory 3D position correction method. We improved the performance of the inventory 3D position estimation by removing frames where the estimated drone position is an outlier. In addition, the method of correcting the estimated inventory 3D position through the relationship between the captured video frame is proposed. * Performance analysis and comparison of the proposed model through the ablation study. We performed the ablation study about the proposed framework to verify that the proposed correction method improves the performance of 3D position estimation of the inventory. The 3D position estimation performance of the inventory was evaluated using videos captured in various real environments. Although inventory management automation studies using drones have been conducted, the 3D position estimation of the inventory based on deep learning model and the error correction method using the characteristics of the video frame are new to the best of the authors' knowledge. The rest of our research is structured as follows. Section 2 describes the background and reviews previous studies on drone-based inventory management, position estimation, and image segmentation. Section 3 details the proposed 3D position estimation framework of inventory, including the overall architecture. Section 4 describes the generated dataset and evaluates the QR code decoding performance of the trained segmentation model and the 3D position estimation performance of the proposed framework.