[1]韩家明,杨忠,陈聪,等.无人机视觉导航着陆标识检测与分割方法[J].应用科技,2020,47(4):1-7,13.[doi:10.11991/yykj.202002013]
 HAN Jiaming,YANG Zhong,CHEN Cong,et al.Detection and segmentation method for the landmark based on UAV visual navigation[J].Applied science and technology,2020,47(4):1-7,13.[doi:10.11991/yykj.202002013]
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年4期
页码:
1-7,13
栏目:
智能科学与技术
出版日期:
2020-07-05

文章信息/Info

Title:
Detection and segmentation method for the landmark based on UAV visual navigation
作者:
韩家明1 杨忠1 陈聪1 张秋雁2 张驰1 赖尚祥1 李宏宸1 方千慧1
1. 南京航空航天大学 自动化学院,江苏 南京 211106;
2. 贵州电网有限责任公司 电力科学研究院,贵州 贵阳 550002
Author(s):
HAN Jiaming1 YANG Zhong1 CHEN Cong1 ZHANG Qiuyan2 ZHANG Chi1 LAI Shangxiang1 LI Hongchen1 FANG Qianhui1
1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Electric Power Research Institute , Guizhou Power Grid Co., Ltd., Guiyang 550002, China
关键词:
无人机视觉导航着陆标识目标检测YOLO特征增强网络裁剪图像分割
Keywords:
UAVvisual navigationlandmarkobject detectionYOLOfeature enhancementnetwork slimmingimage segmentation
分类号:
TP751
DOI:
10.11991/yykj.202002013
文献标志码:
A
摘要:
为实现基于视觉导航无人机自主着陆任务,提出一种无人机视觉导航着陆标识检测与分割方法。在Tiny-YOLO网络基础上融入自下而上的特征增强结构,得到Mark-YOLO网络。针对无人机硬件平台计算能力不足的问题,对网络模型进行裁剪,减少网络模型的参数量;对目标检测算法提取到的无人机着陆标识进行图像分割处理,获取降落标识的轮廓信息。实验结果表明:本文提出的Mark-YOLO算法具有更高的准确率;裁剪后的网络模型具有更少的参数量与更小的权重,且检测到的着陆标识通过图像分割方法处理后,可取得良好的分割效果。
Abstract:
To realize the autonomous landing task of UAV based on visual navigation, we propose a method for detection and segmentation of UAV visual navigation landmark. The Mark-YOLO network is obtained on the basis of Tiny-YOLO network and the bottom-up feature enhancement structure. Aiming at the problem of insufficient computing power of UAV platform, the network model is slimmed to reduce amount of the network parameters; the UAV landing mark extracted by the target detection algorithm is processed by image segmentation to obtain the contour information of the landmark. The experimental results show that the Mark-YOLO algorithm proposed in this paper has higher accuracy, the slimmed network model has less parameters and less weight, and the detected landmark could obtain outstanding segmentation results after being processed by image segmentation.

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备注/Memo

备注/Memo:
收稿日期:2020-02-29。
基金项目:国家自然科学基金项目(61473144);中国南方电网有限责任公司科技项目(066600KK52170074)
作者简介:韩家明,男,博士研究生;杨忠,男,教授,博士生导师
通讯作者:杨忠,E-mail:Yangzhong@nuaa.edu.cn
更新日期/Last Update: 2020-11-27