一种基于深度推理的机载目标检测定位系统

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2632/1/012019
Xiao Hu, Shenfu Pan, Dongdong Li, Long Feng, Yuan Zhao
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引用次数: 0

摘要

近年来,随着传感器、通信网络、深度学习的发展,无人机在目标检测、跟踪、定位等领域得到了广泛的应用。然而,任务执行效率低下,一些复杂的算法仍然需要依赖大型服务器,这在救援和流量调度任务中是无法容忍的。设计能够在机载计算机上运行的快速算法可以有效地解决这一问题。本文提出了一种无人机目标检测与定位系统。我们将基于YOLOX和Swin Transformer的改进目标检测算法ST-YOLO与视觉定位算法相结合,利用TensorRT将其部署在机载端,实现无人机飞行过程中目标的检测与定位。现场实验表明,所建立的系统和算法是有效的。
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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.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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