中圖分類號(hào)： TN014,；TP183 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200280 中文引用格式： 李國(guó)強(qiáng),，彭熾剛，汪勇,，等. 基于深度學(xué)習(xí)的桿塔三維姿態(tài)實(shí)時(shí)估計(jì)[J].電子技術(shù)應(yīng)用,，2021，47(6)：87-91,，95. 英文引用格式： Li Guoqiang,，Peng Chigang，Wang Yong,，et al. Real-time estimation of three-dimensional attitude of towers based on deep learning[J]. Application of Electronic Technique,，2021，47(6)：87-91,，95.
Real-time estimation of three-dimensional attitude of towers based on deep learning
Li Guoqiang1,，Peng Chigang1，Wang Yong1，Xiang Dongwei2,，Yang Chengcheng2
Abstract： According to the current aerial image tower identification algorithm of UAV, it is common for UAV to obtain the original remote viewing image data of the tower through tilt photography technology, and identify the tower in the rest image data through machine learning training.Among them, there are some problems such as slow source of image data needed for machine training and two-dimensional identification of the tower in the picture.In this paper, an algorithm based on deep-object-pose is proposed for real-time aerial aerial aerial aerial recognition of the three-dimensional attitude of the tower.Firstly, image data is synthesized by three-dimensional platform.Secondly, deep-object-pose training and treatment were carried out.Then test the real picture data or real-time video, to achieve intelligent recognition of the tower's three-dimensional attitude information.The experimental results show that this algorithm will provide a new idea for uav to automatically find the target of tower and intelligent fine inspection.
Key words : Deep-Object-Pose,；3D attitude recognition of tower,；UAV；aerial image
