[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(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年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

文章信息/Info

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 robotdeep learningtarget detectionindoor window detectionCenter-NetGhost blockattention mechanismembedded 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.

参考文献/References:

[1] 沈乐, 李桂清, 冼楚华, 等. 室内3D点云模型的门窗检测[J]. 计算机辅助设计与图形学学报, 2019, 31(9):1494-1501
SHEN Le, LI Guiqing, XIAN Chuhua, et al. Door and window detection in 3D point cloud of indoor scenes[J]. Journal of computer-aided design & computer graphics, 2019, 31(9):1494-1501
[2] ALI H, SEIFERT C, JINDAL N, et al. Window detection in facades[C]//14th International Conference on Image Analysis and Processing (ICIAP 2007). Modena, Italy, 2007:837-842.
[3] 孔倩倩, 赵辽英, 张莉. 基于图像轮廓分析的室内窗户检测[J]. 计算机与现代化, 2018(4):56-61
KONG Qianqian, ZHAO Liaoying, ZHANG Li. Indoor window detection based on image contour analysis[J]. Computer and modernization, 2018(4):56-61
[4] 缪君, 储珺, 张桂梅. 基于图像边缘与玻璃属性约束的窗户检测[J]. 图学学报, 2015, 36(5):776-782
MIAO Jun, CHU Jun, ZHANG Guimei. Window detection based on constraints of image edges and glass attributes[J]. Journal of graphics, 2015, 36(5):776-782
[5] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015:1440-1448.
[6] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6):1137-1149.
[7] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9):1904-1916.
[8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:779-788.
[9] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD:single shot MultiBox detector[C]//14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016:21-37.
[10] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017:2999-3007.
[11] ZHU Chenchen, HE Yihui, SAVVIDES M. Feature selective anchor-free module for single-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019:840-849.
[12] LAW H, DENG Jia. CornerNet:detecting objects as paired keypoints[C]//Proceedings of the 15th European Conference on Computer Vision (ECCV). Munich, Germany, 2018:765-781.
[13] ZHANG Shifeng, CHI Cheng, YAO Yongqiang, et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020:9756-9765.
[14] DUAN Kaiwen, BAI Song, XIE Lingxi, et al. CenterNet:keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South), 2019:6568-6577.
[15] TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS:fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South), 2019:9626-9635.
[16] HAN Kai, WANG Yunhe, TIAN Qi, et al. GhostNet:more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020:1577-1586.
[17] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:7132-7141.
[18] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:770-778.
[19] NEWELL A, YANG Kaiyu, DENG Jia. Stacked hourglass networks for human pose estimation[C]//14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016:483-499.
[20] YU F, WANG Dequan, SHELHAMER E, et al. Deep layer aggregation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:2403-2412.
[21] HUANG Gao, LIU Shichen, VAN DER MAATEN L, et al. CondenseNet:an efficient DenseNet using learned group convolutions[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:2752-2761.
[22] PAN Junting, SAYROL E, GIRO-I-NIETO X, et al. Shallow and deep convolutional networks for saliency prediction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:598-606.
[23] ZHU Xizhou, HU Han, LIN S, et al. Deformable ConvNets V2:more deformable, better results[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019:9300-9308.
[24] SHENG Tao, FENG Chen, ZHUO Shaojie, et al. A quantization-friendly separable convolution for mobilenets[C]//2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2). Williamsburg, USA, 2018:14-18.
[25] ZHANG Xiangyu, ZHOU Xinyu, LIN Mengxiao, et al. Shufflenet:an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6848-6856.
[26] CHOLLET F. Xception:deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:1800-1807.

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备注/Memo

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