基于深度学习的杆塔三维姿态实时估计  

Real-time estimation of three-dimensional attitude of towers based on deep learning

作　　者：李国强 彭炽刚 汪勇 向东伟 杨成城 Li Guoqiang;Peng Chigang;Wang Yong;Xiang Dongwei;Yang Chengcheng(Machine Operation Center,Guangdong Power Grid Co.,Ltd.,Guangzhou 510062,China;Wuhan Huizhuohang Technology Co.,Ltd.,Wuhan 430070,China)

机构地区：[1]广东电网有限责任公司机巡作业中心,广东广州510062 [2]武汉汇卓航科技有限公司,湖北武汉430070

出　　处：《电子技术应用》2021年第6期87-91,95,共6页Application of Electronic Technique

基　　金：广东电网有限责任公司科技项目(GDKJXM20184737)。

摘　　要：针对目前无人机航拍影像杆塔识别算法中,普遍是无人机通过倾斜摄影技术获取到杆塔的原始遥观影像数据,经过机器学习训练,识别其余图片数据中的杆塔。其中存在获取机器训练所需的图片数据来源缓慢、只能二维识别图片中杆塔等问题。提出了基于深度学习的杆塔三维姿态实时估计的算法。首先,通过三维平台合成影像数据;其次,通过Deep-Object-Pose训练及其处理;然后测试真实的图片数据或者实时视频,达到智能识别杆塔的三维空间姿态信息。该算法为无人机自动寻找杆塔目标和智能精细化巡检提供新的思路。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.

关 键 词：Deep-Object-Pose 杆塔三维空间姿态识别 无人机 航拍影像 

分 类 号：TN014[电子电信—物理电子学] TP183[自动化与计算机技术—控制理论与控制工程]