## 2 Methods
### Mono-Colored Spherical Object Detection Algorithm  
Landing control strongly depends on the performance of the detection algorithm. To reduce the latency of the detection algorithm, a simple detection method could be used (e.g., HSV filtering detection). However, simple detection methods are vulnerable to different illumination conditions. For example, side illumination can create shadows and cause variations in the HSV color space, even for mono-colored target objects. Additionally, backlight can turnish the color of the target object, which in turn can degrade the performance of the HSV filter-based detection algorithm. Figure 4 shows some situations in which the HSV detection fails (e.g., only a portion of the target object is detected). To prevent such failures, a new hybrid detection algorithm that provides accurate and robust spherical object detection with low latency was developed in this study. This algorithm is characterized with the following features: integration of color filtering and edge detection algorithm, use of cropped images for low latency, and an adaptive detection strategy robust to variations in lighting conditions. First, the newly developed algorithm conducts both HSV filtering and the circle Hough transform (CHT) [33]. In general, CHT is more accurate than HSV filtering; however, HSV filtering is faster than CHT. Furthermore, CHT cannot detect a spherical object when there is a large number of edges, which are irrelevant to the spherical object. Moreover, the detection fails when the edge of the object is blurred in the captured image due to the  
Figure 4: Inaccurate detection with HSV filtering under harsh lighting conditions. (**a**) Dark space with insufficient directional lighting; (**b**) bright space with excessive backlight. Figure 3: MAV PD control. sudden movement of the MAV. To overcome these accuracy and latency issues, both HSV filtering and CHT are used in the new algorithm. Second, a cropping technique was introduced to reduce the CHT computation time. The classical CHT calculates all edges detected in the entire image. However, if all pixels in the image are investigated for detection, CHT would require a long computation time for real-time MAV control. Thus, in the new algorithm, CHT is applied on a cropped image, not on the entire image. Specifically, a spherical object is detected via HSV filtering, as shown in Figure 5a. If a lighting condition is not desirable for object detection, the HSV filter is likely to detect just a portion of the spherical object; it cannot detect the whole body of the spherical object. Then, a square image is cropped, as shown in Figure 5b. The center of the cropped image is determined as the center position of the object, and its width and height are determined as twice the diameter of the detected object. Next, the position and size of the spherical object are detected via CHT on the cropped image, as shown in Figure 5c. Note that conversion into a grayscale image and a bilateral filter were also used for noise reduction, while preserving the sharp edges of the image. This approach leads to an accurate and fast spherical object detection. Finally, the new algorithm adaptively determines the size and position of the spherical object based on the following scenarios:  
* Scenario 1. If a sphere is detected via CHT, the size and position estimated by the CHT are used in the control. * Scenario 2. If the CHT fails to find a spherical object and the MAV approaches the platform, the position calculated by the HSV filter is used. The radius is determined as the larger value between the radius predicted via HSV filtering and the radius estimated in the previous time. This criterion is required because the radius predicted via HSV filtering is usually smaller than the true radius. This inaccurate radius estimation, which is smaller than the true radius, leads to a very high approaching speed in STEP 2. Consequently, the MAV is likely to collide with the spherical object.