无人机巡检中的边缘AI识别应用研究

Application of edged AI recognition in UAV inspection

随着无人机技术的快速发展,无人机在巡检领域的应用日益广泛,特别是在电力、建筑等行业。无人机巡检可以大幅提高巡检效率,减少人力成本,并能在复杂环境中进行高效作业。然而,传统的无人机巡检方法依赖于人工识别,存在效率低、易出错等问题。无人机技术结合深度学习目标识别的应用,可以显著提高巡检效率和准确性。本文旨在开发一种基于深度学习的无人机巡检边缘嵌入式目标识别模型,将其部署在无人机边缘设备上,实时地从航拍视频流中检测和识别目标,为巡检提供高效、精准的解决方案。

With the rapid development of unmanned aerial vehicle(UAV)technology,the application of UAV in the field of in⁃spection is increasingly extensive,especially in the power,construction,and other industries.UAV inspection can greatly improve in⁃spection efficiency,reduce labor costs,and carry out efficient operations in complex environments.However,the traditional UAV in⁃spection method relies on manual recognition,which has some problems such as low efficiency and easy error.UAV technology combined with the application of deep learning object recognition can significantly improve the efficiency and accuracy of inspection.This paper aims to develop a deep learning-based embedded target recognition model for UAV inspection edge,deploy it on UAV edge equipment,and de⁃tect and identify targets from aerial video streams in real time,to provide an efficient and accurate solution for inspection.