## 1 Introduction  
The flight capabilities and applications of unmanned aerial vehicles (UAVs), along with battery technologies, have greatly expanded over the past decade. Accordingly, various UAV applications have been developed, such as surveillance [1, 2], package delivery [3], hazardous rescue missions [4, 5], and even personal transportation [6]. Micro UAVs (MAVs) are widely used for these applications because of their small size, stable control, and switchness. These advantages enable MAVs to perform autonomous tasks in situations where ground vehicles cannot operate [7, 8]. One of the main challenges in realizing a fully autonomous UAV is autonomous landing [9]. During landing, UAVs are vulnerable to disturbances, and small errors can lead to catastrophic results. The global positioning system (GPS) is commonly used for the positioning and navigation of UAVs [10, 11]. Although GPS can guide UAVs to the area around the landing site, it cannot be used as an indicator for landing because its accuracy is not sufficiently high for precise landing. Moreover, GPS cannot be used in closed environments, such as tunnels and indoor and underground spaces. Consequently, many studies have implemented additional sensors for UAV landing control, including red, green and blue (RGB) vision systems [12, 13, 14], internal navigation systems [15], ultra-wideband beacons [16], ground-based infrared cameras [17, 18], and radar stations [19, 20]. Among positioning sensors, RGB cameras have been widely adopted in previous studies. Specifically, images obtained by a camera facing the ground have been used for landing control [12, 13, 14]. [PERSON] et al. [21] proposed a ground-based stereovision system for UAVhovering and landing. Simultaneous localization and mapping [22; 23; 24], which map the landscape for navigation and landing in unknown landscapes, have also been developed. Multi-visual sensor systems improve the robustness and accuracy of UAV landing. A front-mounted camera enables the UAV to detect the distance between the UAV and the landing platform. However, this camera cannot acquire the precise position information of the landing spot. To address this problem, previous studies have used active gimbal systems to control the angle of the UAV camera [25; 26]. Other studies have installed two cameras on UAVs facing different directions [27; 28]. Although dual-camera systems provide the visual data required for landing, images acquired by the cameras can fluctuate owing to the pitch and roll motion of the UAV. Thus, a gimbal system is indispensable for landing. However, considering the small thrust force of MAVs, the loading capacity of MAVs may not be sufficient for a gimbal system. Furthermore, the embedded controller of UAVs requires additional computational power to process images from a dual-camera system, which is not feasible for MAVs. Thus, this study used an MAV that carries a single vision sensor (a monocular RGB camera). To perform a precise MAV landing, an upward-facing camera was installed on the landing platform for MAV positioning, and multiple objects were placed to provide information regarding the relative position between the MAV and the platform. Note that the image processing computation for the secondary camera can be conducted using a device connected to the platform, thereby alleviating the computational load of the MAV. Detection of a target object on the landing platform plays a crucial role in MAV control. Recent detection algorithms based on deep neural networks have exhibited high detection accuracy [29; 30; 31; 32]. However, highly accurate models typically require high-end computing hardware. Considering the space and weight limitations of MAVs, a simple detection scheme is required. Therefore, a computationally less expensive object detection algorithm was developed in this study. The new algorithm integrates color filtering and shape detection algorithms. The detection result in a previous time step is also used for detection accuracy enhancement.