부산대학교 도서관

Unnamed Aerial Vehicle (UAV) - based remote sensing is a promising technology that is being applied for inspecting live scenes from high altitudes (e.g., for surveillance and recognizing emergencies). The evolution of hardware and software technologies in the last few years has generated additional interest in embedded systems research and its implementation in energy-independent UAVs for remote sensing. Alongside, ultra-high-resolution optical sensors are mandatory for acquiring high-resolution images which are necessary for accurate object detection from a distance (e.g., 1,000 meters). The processing of ultra-high-resolution images (e.g., 4K or 8K) is beyond the typical resolutions which are used for object detection (e.g., < 2K) emerging a necessity for special treatment in order to succeed a fast object detection. We propose a three-step approach deployed on a Docker runtime environment in an Nvidia Jetson AGX Xavier board. To support fast object detection, the captured images are split into K parts processed in parallel in separate containers running the YOLOv5 object detection algorithm. A final detection is constructed based on each one of the K detections. The experimental results are a good support to our claims of efficiency: the method can achieve close to real-time object detection for ultra-high (i.e., 8K) resolution images (i.e., in less than 1 second per frame).