[1]刘召,张黎明,耿美晓,等.基于改进的Faster R-CNN高压线缆目标检测方法[J].智能系统学报,2019,14(04):627-634.[doi:10.11992/tis.201905026]
 LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14(04):627-634.[doi:10.11992/tis.201905026]
点击复制

基于改进的Faster R-CNN高压线缆目标检测方法(/HTML)
分享到:

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

卷:
第14卷
期数:
2019年04期
页码:
627-634
栏目:
出版日期:
2019-07-02

文章信息/Info

Title:
Object detection of high-voltage cable based on improved Faster R-CNN
作者:
刘召1 张黎明2 耿美晓1 么军2 张金禄2 胡益菲2
1. 清研同创机器人(天津)有限公司, 天津 300300;
2. 国网天津市电力公司, 天津 300010
Author(s):
LIU Zhao1 ZHANG Liming2 GENG Meixiao1 YAO Jun2 ZHANG Jinlu2 HU Yifei2
1. Tsinghua Tongchuang Robot Co.,Ltd, Tianjin 300300, China;
2. State Grid Tianjin Electric Power Company, Tianjin 300010, China
关键词:
目标检测深度学习高压线缆复杂背景小目标带电作业Faster R-CNN区域候选
Keywords:
object detectiondeep learninghigh-voltage cablecomplicated backgroundsmall objectlive workingFaster R-CNNregion proposal
分类号:
TP18;TP391
DOI:
10.11992/tis.201905026
摘要:
利用带电作业机器人取代人类的手动作业,可以有效地减少高电压、强电场对人体的危害,大大提高作业的效率。为解决带电作业机器人在复杂背景环境中对线缆目标的智能检测问题,提出基于改进的Faster R-CNN高压线缆目标检测方法。为了提高网络提取图像高级特征的能力,引入跳转连接并调整激活层、卷积层的顺序;然后对候选框生成机制进行改进,提升网络对小目标检测的性能;最后利用ROI池化层提取每个区域的特征,同时完成分类和框回归任务。通过构建高压线缆图像数据集,基于改进的Faster R-CNN模型进行大量实验,最后取得了较好的精度和较快的速度。
Abstract:
The use of live working robots to replace human manual operation can effectively reduce the harm of a high-voltage and strong electric field to the human body and considerably improve the working efficiency. To solve the intelligent high-voltage cable object detection problem for live working robots under a complicated background environment, a high-voltage cable object detection method based on the improved Faster R-CNN is proposed. To improve the capability of extracting the high-level features of images in the network, skip connections are introduced and the order of the activation and convolution layers is adjusted. Then, the proposal bounding box generation mechanism is improved to enhance the performance of the proposed method for small object detection. Finally, the features of each region are extracted using the ROI pooling layers, and the classification and bounding box regression tasks are accomplished at the same time. Through the construction of high-voltage cable image datasets and the performance of numerous experiments based on the improved Faster R-CNN model, good accuracy and fast speed have been achieved.

参考文献/References:

[1] 赵玉良, 戚晖, 陈凡明, 等. 高压带电作业机器人专用遥控剥皮器的研制[J]. 微计算机信息, 2010, 26(32):146-147, 119 ZHAO Yuliang, QI Hui, CHEN Fanming, et al. Design on the remote controlled electric-driving remover for live working robot[J]. Microcomputer information, 2010, 26(32):146-147, 119
[2] 王振利, 鲁守银, 李健, 等. 高压带电作业机器人视觉伺服系统[J]. 制造业自动化, 2013, 35(7):69-72 WANG Zhenli, LU Shouyin, LI Jian, et al. Vision servo system for high-voltage live working robot[J]. Manufacturing automation, 2013, 35(7):69-72
[3] 于进勇, 丁鹏程, 王超. 卷积神经网络在目标检测中的应用综述[J]. 计算机科学, 2018, 45(S2):17-26 YU Jinyong, DING Pengcheng, WANG Chao. Overview:application of convolution neural network in object detection[J]. Computer science, 2018, 45(S2):17-26
[4] CAI Zhaowei, VASCONCELOS N. Cascade R-CNN:delving into high quality object detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6154?6162.
[5] 宋焕生, 张向清, 郑宝峰, 等. 基于深度学习方法的复杂场景下车辆目标检测[J]. 计算机应用研究, 2018, 35(4):1270-1273 SONG Huansheng, ZHANG Xiangqing, ZHENG Baofeng, et al. Vehicle detection based on deep learning in complex scene[J]. Application research of computers, 2018, 35(4):1270-1273
[6] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[7] 毕晓君, 冯雪赟. 基于改进深度学习模型C-GRBM的人体行为识别[J]. 哈尔滨工程大学学报, 2018, 39(1):156-162 BI Xiaojun, FENG Xueyun. Human action recognition based on improved depth learning model C-GRBM[J]. Journal of Harbin Engineering University, 2018, 39(1):156-162
[8] 龙慧, 朱定局, 田娟. 深度学习在智能机器人中的应用研究综述[J]. 计算机科学, 2018, 45(S2):43-47, 52 LONG Hui, ZHU Dingju, TIAN Juan. Research on deep learning used in intelligent robots[J]. Computer science, 2018, 45(S2):43-47, 52
[9] 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望[J]. 自动化学报, 2017, 43(8):1289-1305 ZHANG Hui, WANG Kunfeng, WANG Feiyue. Advances and perspectives on applications of deep learning in visual object detection[J]. Acta automatica sinica, 2017, 43(8):1289-1305
[10] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014:580?587.
[11] 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.
[12] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015:1440?1448.
[13] 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.
[14] 莫宏伟, 汪海波. 基于Faster R-CNN的人体行为检测研究[J]. 智能系统学报, 2018, 13(6):967-973 MO Hongwei, WANG Haibo. Research on human behavior detection based on Faster R-CNN[J]. CAAI transactions on intelligent systems, 2018, 13(6):967-973
[15] 曹宇剑, 徐国明, 史国川. 基于旋转不变Faster R-CNN的低空装甲目标检测[J]. 激光与光电子学进展, 2018, 55(10):101501 CAO Yujian, XU Guoming, SHI Guochuan. Low altitude armored target detection based on rotation invariant faster R-CNN[J]. Laser and optoelectronics progress, 2018, 55(10):101501
[16] 魏湧明, 全吉成, 侯宇青阳. 基于YOLO v2的无人机航拍图像定位研究[J]. 激光与光电子学进展, 2017, 54(11):111002 WEI Yongming, QUAN Jicheng, HOU Yuqingyang. Aerial image location of unmanned aerial vehicle based on YOLO v2[J]. Laser and optoelectronics progress, 2017, 54(11):111002
[17] REN Shaoqing, HE Kaiming, GIRSHICK R, SUN J. 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.
[18] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016:770?778.
[19] XIE Saining, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:5987?5995.

相似文献/References:

[1]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(01):61.
 HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7(04):61.
[2]韩峥,刘华平,黄文炳,等.基于Kinect的机械臂目标抓取[J].智能系统学报,2013,8(02):149.[doi:10.3969/j.issn.1673-4785.201212038]
 HAN Zheng,LIU Huaping,HUANG Wenbing,et al.Kinect-based object grasping by manipulator[J].CAAI Transactions on Intelligent Systems,2013,8(04):149.[doi:10.3969/j.issn.1673-4785.201212038]
[3]张媛媛,霍静,杨婉琪,等.深度信念网络的二代身份证异构人脸核实算法[J].智能系统学报,2015,10(02):193.[doi:10.3969/j.issn.1673-4785.201405060]
 ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10(04):193.[doi:10.3969/j.issn.1673-4785.201405060]
[4]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(01):1.[doi:10.3969/j.issn.1673-4785.201403072]
 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10(04):1.[doi:10.3969/j.issn.1673-4785.201403072]
[5]韩延彬,郭晓鹏,魏延文,等.RGB和HSI颜色空间的一种改进的阴影消除算法[J].智能系统学报,2015,10(5):769.[doi:10.11992/tis.201410010]
 HAN Yanbin,GUO Xiaopeng,WEI Yanwen,et al.An improved shadow removal algorithm based on RGB and HSI color spaces[J].CAAI Transactions on Intelligent Systems,2015,10(04):769.[doi:10.11992/tis.201410010]
[6]曾宪华,易荣辉,何姗姗.流形排序的交互式图像分割[J].智能系统学报,2016,11(1):117.[doi:10.11992/tis.201505037]
 ZENG Xianhua,YI Ronghui,HE Shanshan.Interactive image segmentation based on manifold ranking[J].CAAI Transactions on Intelligent Systems,2016,11(04):117.[doi:10.11992/tis.201505037]
[7]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11(04):279.[doi:10.11992/tis.201603026]
[8]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567.[doi:10.11992/tis.201511028]
 LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11(04):567.[doi:10.11992/tis.201511028]
[9]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728.[doi:10.11992/tis.201611021]
 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11(04):728.[doi:10.11992/tis.201611021]
[10]王亚杰,邱虹坤,吴燕燕,等.计算机博弈的研究与发展[J].智能系统学报,2016,11(6):788.[doi:10.11992/tis.201609006]
 WANG Yajie,QIU Hongkun,WU Yanyan,et al.Research and development of computer games[J].CAAI Transactions on Intelligent Systems,2016,11(04):788.[doi:10.11992/tis.201609006]
[11]葛园园,许有疆,赵帅,等.自动驾驶场景下小且密集的交通标志检测[J].智能系统学报,2018,13(03):366.[doi:10.11992/tis.201706040]
 GE Yuanyuan,XU Youjiang,ZHAO Shuai,et al.Detection of small and dense traffic signs in self-driving scenarios[J].CAAI Transactions on Intelligent Systems,2018,13(04):366.[doi:10.11992/tis.201706040]
[12]莫宏伟,汪海波.基于Faster R-CNN的人体行为检测研究[J].智能系统学报,2018,13(06):967.[doi:10.11992/tis.201801025]
 MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13(04):967.[doi:10.11992/tis.201801025]

备注/Memo

备注/Memo:
收稿日期:2019-05-14。
基金项目:天津市智能制造科技重大专项(17ZXZNGX00120).
作者简介:刘召,男,1979年生,博士,主要研究方向为机器人及其自动化装备。作为项目负责人和技术骨干完成研究课题10余项,其中,863项目1项,985项目1项,国防科技课题4项,日本学术振兴会科学研究补助金基础研究项目1项。发表学术论文10余篇;张黎明,男,1969年生,高级技师,主要研究方向为智能配电网。个人获24项国家专利,带领团队为公司实现技术革新400多项,获国家专利158项,20余项填补智电网建设空白;耿美晓,女,1990年生,硕士,主要研究方向为图像处理、机器人学习与人工智能。
通讯作者:耿美晓.E-mail:lin@thtcrobot.com
更新日期/Last Update: 2019-08-25