[1]毛莺池,唐江红,王静,等.基于Faster R-CNN的多任务增强裂缝图像检测方法[J].智能系统学报,2021,16(2):286-293.[doi:10.11992/tis.201910004]
 MAO Yingchi,TANG Jianghong,WANG Jing,et al.Multi-task enhanced dam crack image detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2021,16(2):286-293.[doi:10.11992/tis.201910004]
点击复制

基于Faster R-CNN的多任务增强裂缝图像检测方法

参考文献/References:
[1] 苏南. 我国200米级高坝密集, 安全风险不可轻视[EB/OL]. (2017-11-09).https://www.thepaper.cn/newsDetail_forward_1858088.
[2] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge, USA, 2015:91-99.
[3] HABER E, RUTHOTTO L, HOLTHAM E, et al. Learning across scales-a multiscale method for convolution neural networks[C]//Proceedings of the 23nd AAAI Conference on Artificial Intelligence. 2017.
[4] GERBER D, MEIER S, KELLERMANN W. Efficient target activity detection based on recurrent neural networks[C]//Proceedings of 2017 Hands-free Speech Communications and Microphone Arrays. San Francisco, USA, 2017:46-50.
[5] 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 (CVPR). Columbus, USA, 2014:580-587.
[6] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile, 2015:1440-1448.
[7] LIU WEI, ANGUELOV D, ERHAN D, et al. SSD:single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands, 2016:21-37.
[8] REDMON J, FARHADI A. YOLO9000:better, faster, stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:6517-6525.
[9] KANG H H, LEE S W, YOU S H, et al. Novel vehicle detection system based on stacked DoG kernel and AdaBoost[J]. PLoS one, 2018, 13(3):e0193733.
[10] DAI Wenyuan, YANG Qiang, XUE Guirong, et al. Boosting for transfer learning[C]//Proceedings of the 24th International Conference on Machine Learning. New York, USA, 2007:193-200.
[11] AL-STOUHI S, REDDY C K. Adaptive boosting for transfer learning using dynamic updates[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Germany, 2011:60-75.
[12] 郭勇. 基于单源及多源的迁移学习方法研究[D]. 西安:西安电子科技大学, 2013.
GUO Yong. Research of transfer learning based on single-source and multi-source[D]. Xi’an:Xidian University, 2013.
[13] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:770-778.
[14] WICAKSONO Y A, RIZALDY A, FAHRIAH S, et al. Improve image segmentation based on closed form matting using K-means clustering[C]//Proceedings of 2017 International Seminar on Application for Technology of Information and Communication. Semarang, Indonesia, 2018:26-30.
[15] AKCAY S, KUNDEGORSKI M E, WILLCOCKS C G, et al. Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery[J]. IEEE transactions on information forensics and security, 2018, 13(9):2203-2215.
[16] RAHMAN M A, WANG Yang. Optimizing intersection-over-union in deep neural networks for image segmentation[C]//Proceedings of 12th International Symposium on Advances in Visual Computing. Las Vegas, USA, 2016:234-244.
[17] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//Proceedings of 13th European Conference on Computer Vision. Zurich, Switzerland, 2014:818-833.
[18] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of 3rd International Conference on Learning Representations. San Diego, USA, 2015.
相似文献/References:
[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(4):627.[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():627.[doi:10.11992/tis.201905026]
[2]莫宏伟,田朋.基于注意力融合的图像描述生成方法[J].智能系统学报,2020,15(4):740.[doi:10.11992/tis.201910039]
 MO Hongwei,TIAN Peng.An image caption generation method based on attention fusion[J].CAAI Transactions on Intelligent Systems,2020,15():740.[doi:10.11992/tis.201910039]
[3]翟永杰,杨旭,赵振兵,等.融合共现推理的Faster R-CNN输电线路金具检测[J].智能系统学报,2021,16(2):237.[doi:10.11992/tis.202012023]
 ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16():237.[doi:10.11992/tis.202012023]
[4]张铭泉,邢福德,刘冬.基于改进Faster R-CNN的变电站设备外部缺陷检测[J].智能系统学报,2024,19(2):290.[doi:10.11992/tis.202207016]
 ZHANG Mingquan,XING Fude,LIU Dong.External defect detection of transformer substation equipment based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2024,19():290.[doi:10.11992/tis.202207016]

备注/Memo

收稿日期:2019-09-15。
基金项目:国家重点研发课题(2018YFC0407105);国家自然科学基金重点项目(61832005);国网新源科技项目(SGTYHT/19-JS-217);华能集团重点研发课题(HNKJ19-H12)
作者简介:毛莺池,教授,博士,博士生导师,主要研究方向为云计算和边缘计算、分布式技术和物联网数据分析。曾获大禹水利科学技术奖一等奖;华能集团科技进步奖二等奖;江苏省科学技术奖三等奖;2018年度江苏省计算机学会优秀科技工作者。发表学术论文50余篇;唐江红,硕士研究生,主要研究方向为图像处理;王静,硕士研究生,主要研究方向为图像处理
通讯作者:唐江红.E-mail:15195897810@163.com

更新日期/Last Update: 2021-04-25
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com