An airborne object detection and location system based on deep inference

Abstract

In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO based on YOLOX and Swin Transformer with the visual positioning algorithm and deploy it on the airborne end by using TensorRT to realize the detection and location of objects during the flight of the drone. Field experiments show that the established system and algorithm are effective.