## 6 Conclusions  
In this paper, a system for detecting multiple color markers by image processing and detecting the direction of a ground station based on the location information of the markers was proposed, and its performance was evaluated for a wireless relay system using a small fixed-wing UAV for image transmission. Though color markers are robust to low resolution and motion blur due to their detection principle, they are prone to false detection in noisy natural images and cannot detect only color markers installed at ground stations. In this study, we proposed a method to detect only the color markers in captured images by calculating the placement patterns of the markers in the image based on the relative positions and coordinates of the markers and cameras, and matching them with the captured images to construct a ground station detection system. Based on the assumed operational environment of the wireless relay system, we determined the performance requirements for low resolution, marker direction detection accuracy, and motion blur, and evaluated whether the constructed ground station detection system achieves the required performance. The results were as follows: (1) the system was able to detect markers with 1.4 times lower resolution than the required performance standard; (2) the system was  
Figure 21: Example of RCM detection failure due to some color markers being hidden (lower right color marker in image not visible).  
able to detect marker direction more accurately than the required performance standard by 0.02 [deg.]. This has enabled us to construct a basic ground station detection system for establishing a wireless relay system using a small fixed-wing UAV.  
Although the proposed method has achieved good results in ground experiments, its performance under flight conditions has not yet been verified. In outdoor flight, changes in ambient light may cause the discoloration of the acquired images, and during circling flight, the wing may hide the color markers due to the bank angle. Additionally, the camera may be subjected to unexpected angular velocities due to aircraft engine vibration. Furthermore, the placement pattern of the color markers depends on the camera's field of view (FoV) and the distance from the markers, which could cause the markers to move out of the FoV and fail to be detected. If RCMs cannot be detected due to these factors, a method is needed either to correct the image using deep learning (DL) or to detect RCMs directly using DL.  
Moreover, there is room for improvement in the onboard computation of small fixed-wing UAVs. For example, dedicated hardware such as an Application-Specific Integrated Circuit (ASIC) could be utilized to reduce the power consumption of the sensor system, extend the UAV's flight time, and improve its operability.  
In future research, it will be important to prioritize flight experiments to verify system performance in real-world environments. Additionally, evaluating system robustness in various flight environments and making adjustments to mitigate potential problems related to visibility and environmental conditions will be critical for future success.  
Conceptualization, K.H.; methodology, K.H. and M.U.; software, K.H.; validation, K.H.; resources, M.U.; writing--original draft preparation, K.H.; writing--review and editing, M.U.; supervision, M.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available as they are to be used in future research.  
The authors declare no conflict of interest.