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The Detection System of Helipad for Unmanned Aerial Vehicle Landing Using YOLO Algorithm

Bhakti Yudho Suprapto, A. Wahyudin, Hera Hikmarika, Suci Dwijayanti

Abstract


The challenge with using the Unmanned Aerial Vehicle (UAV) is when the UAV makes a landing. This problem can be overcome by developing a landing vision through helipad detection. This helipad detection can make it easier for UAVs to land accurately and precisely by detecting the helipad using a camera. Furthermore, image processing technology is used on the image produced by the camera. You Only Look Once (YOLO) is an image processing algorithm developed to detect objects in real-time, and it is the result of the development of one of the Convolutional Neural Network (CNN) algorithm methods. Therefore, in this study the YOLO method was used to detect a helipad in real-time. The models used in the YOLO algorithm were Mean-Shift and Tiny YOLO VOC. The Tiny YOLO VOC model performed better than the Mean-Shift method in detecting helipads. The test results obtained a confidence value of 91.1%, and the system processing speed reached 35 frames per second (fps) in bright conditions and 37 fps in dark conditions at an altitude of up to 20 meters.

Keywords


Object Detection; Helipad; Image Processing; Unmanned Aerial Vehicle (UAV); YOLO Algorithm

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References


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DOI: http://dx.doi.org/10.26555/jiteki.v7i2.20684

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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
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