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[1]洪恺临,曹江涛,姬晓飞.改进Center-Net网络的自主喷涂机器人室内窗户检测[J].智能系统学报,2021,16(3):425-432.[doi:10.11992/tis.202005016]
　HONG Kailin,CAO Jiangtao,JI Xiaofei.Indoor window detection of autonomous spraying robot based on improved CenterNet network[J].CAAI Transactions on Intelligent Systems,2021,16(3):425-432.[doi:10.11992/tis.202005016]

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改进Center-Net网络的自主喷涂机器人室内窗户检测

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 16 期数: 2021年第3期页码: 425-432 栏目: 学术论文—机器感知与模式识别出版日期: 2021-05-05

Title:: Indoor window detection of autonomous spraying robot based on improved CenterNet network

作者:: 洪恺临¹, 曹江涛¹, 姬晓飞²; 1. 辽宁石油化工大学信息与控制工程学院，辽宁抚顺 113001;
2. 沈阳航空航天大学自动化学院，辽宁沈阳 110136

Author(s):: HONG Kailin¹, CAO Jiangtao¹, JI Xiaofei²; 1. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China

关键词:: 喷涂机器人; 深度学习; 目标检测; 室内窗户检测; 中心点网络; Ghost模块; 注意力机制; 嵌入式设备

Keywords:: spraying robot; deep learning; target detection; indoor window detection; Center-Net; Ghost block; attention mechanism; embedded device

分类号:: TP391.1

DOI:: 10.11992/tis.202005016

摘要:: 室内自主喷涂机器人可以实现室内墙面喷涂的自动化以此提升喷涂的效率，减少人力物力的投入。而基于计算机视觉的室内窗户检测算法则是该机器人的关键技术。对于室内窗户检测，由于环境光照、窗户形状和窗户透光属性的存在，传统方法无法得到较好的效果。针对此问题，设计一种基于深度学习的室内窗户检测算法。该算法主要对中心点网络(CenterNet)的特征提取网络进行修改，减少部分卷积操作，使用Ghost模块替换原始的卷积模块，降低特征冗余，并引入注意力机制，让网络尽可能表达重要信息。实验结果表明，改进的CenterNet在不损失网络精度的前提下，大幅度提高了网络的运算速度，使得该检测算法即使在机器人端的嵌入式系统上也可以达到实时检测的效果。

Abstract:: An indoor autonomous spraying robot can realize the automation of indoor wall spraying to improve the efficiency of spraying and reduce the investment of manpower and material resources. The indoor window detection algorithm based on computer vision is the key technology of the robot. For indoor window detection, traditional methods cannot obtain good results owing to the actual scene’s requirements for recognition speed and accuracy as well as the presence of lighting in the environment, shape of the window, and light transmission properties of the window. To solve this problem, an indoor window detection algorithm based on deep learning is designed. This algorithm mainly modifies the backbone feature extraction of the CenterNet network, reduces part of the convolution operation, replaces the original convolution module with ghost block, reduces the redundancy feature, and introduces an attention mechanism to keep the network under a limited number of parameters that express important information as much as possible. The experimental results show that the improved CenterNet algorithm greatly improves the operation speed of the network without losing the accuracy of the network so that the network can achieve a real-time detection effect even on the embedded system of the robot.

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备注/Memo

收稿日期:2020-05-12。
基金项目:国家自然科学基金项目(61673199)；辽宁省科技公益研究基金项目(2016002006)
作者简介:洪恺临，硕士研究生，主要研究方向为计算机视觉、深度学习;曹江涛，教授，博士，主要研究方向为为智能方法及其应用、视频分析与处理。主持国家自然科学基金项目1项、辽宁省自然科学基金项目1项。参与编著英文专著2部，发表学术论文50余篇;姬晓飞，副教授，博士，主要研究方向为视频分析与处理、模式识别理论。主持国家自然科学基金项目1项、辽宁省自然科学基金项目1项。参与编著英文专著2部，发表学术论文40余篇
通讯作者:姬晓飞.E-mail:jixiaofei7804@126.com

更新日期/Last Update: 2021-06-25

改进Center-Net网络的自主喷涂机器人室内窗户检测 PDF下载HTML

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改进Center-Net网络的自主喷涂机器人室内窗户检测

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