High-definition technology of AI inference scheme for object detection on edge/terminal

Abstract

To detect a wide range of objects with one camera at once, real-time object detection in high-definition video is required in video artificial intelligence (AI) applications for edge/terminal, such as beyond-visual-line-of-sight (BVLOS) drone flight. Although various AI inference schemes for object detection (e.g., you-only-look-once (YOLO)) have been proposed, they typically have limitations on the input image size and thus need to shrink the input high-definition image down to the limit. This makes small objects collapsed and undetectable. This paper presents our proposal technology for solving this problem and its effective implementation, where multiple object detectors cooperate to detect small and large objects in high-definition video such as full HD and 4K.