Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

Abstract

This research is a form of development of object detection capabilities on wheeled soccer robots using an omnidirectional camera with the You Only Look Once (YOLO) method, where the results show that the robot can detect more than one object, namely the ball and the goal on the green field. This study uses the KRSBI-Wheeled UAD robot using an omnidirectional camera as a tool to carry out the detection process and then uses OpenCV 4.0, Deep Learning, and a laptop as a place to create a detection model, as well as balls and goals as objects to be detected. The results obtained from this study are that the two types of YOLO models tested, namely YOLOv3 and YOLOv3-Tiny, can detect ball and goal objects in two different types of frame sizes, namely 320×320 and 416×416, which can be seen from the performance of the YOLOv3 model which has an mAP value of 76%. On the 320×320 frame and an mAP value of 87.5% in the 416×416 frame, then the YOLOv3-Tiny model has an mAP value of 68.1% in the 320×320 frame, and an mAP value of 75.5% in the 416×416 frame where the YOLOv3 model can detect both object class is much more stable compared to YOLOv3-Tiny.