無人航空機のリスク低減のための魚眼カメラによる飛行障害物検知

抄録

Uncrewed Aerial Systems (UAS) must be capable of autonomously detecting and avoiding obstacles for safe operation. Object recognition with a wide 360-degree field of view is essential to achieve this objective. However, conventional object detection algorithms often cannot handle the distortion of fisheye camera images. This research focuses on developing a deep learning-based object recognition system using yolov5, which is specialized for fisheye camera images. We aim to improve detection accuracy by building a dataset of labeled fisheye images and fine-tuning the model. This research will contribute to developing autonomous navigation for unmanned aerial vehicles.