改进的基于区域卷积神经网络的微操作系统目标检测方法

doi:10.16451/j.cnki.issn1003-6059.201802005

摘要
图/表
参考文献
相关文章 (11)

全文: PDF (1109 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要传统的目标检测方法不能有效检测微操作系统中部分受遮挡或多种姿态的目标,因此文中采用改进的基于区域卷积神经网络的Faster-RCNN检测算法,用于微操作系统中部分受遮挡或多种姿态的目标检测.在原始Faster-RCNN的基础上,使用在图像分类任务中性能优越的深度残差网络作为检测算法的主框架,并且引入防止正负样本不均衡的在线困难样本挖掘策略以提高网络性能.实验表明,这种改进的基于区域卷积神经网络方法能有效识别部分受遮挡和不同姿态的目标,相比传统方法,文中方法对环境适应性更强,速度更快,具有实际应用价值.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	彭刚
	杨诗琪
	黄心汉
	苏豪

关键词 ：微操作系统, 目标检测, 区域卷积神经网络, 深度残差网络, 在线困难样本挖掘

Abstract：In micro-operating system, traditional object detection method cannot detect the objects with partial occlusion and multiple poses, and thus an improved faster region convolutional neural network(Faster RCNN) is adopted to solve the problem. On the basis of original Faster RCNN, deep residual network exhibiting excellent performance in image classification is introduced as the framework of the algorithm, and online hard example mining strategy to enhance the performance by alleviating the imbalance between positive and negative examples is employed. The experimental results manifest that the proposed method can detect objects with partial occlusion and multiple poses effectively. The proposed method shows strong adaptability to environment, responds quickly compared with traditional methods, and thus the practicality of it is verified.

Key words： Micro-operating System Object Detection Region Convolutional Neural Network Deep Residual Network Online Hard Example Mining

收稿日期: 2017-09-18

ZTFLH:

TP 242

基金资助:国家高科技研究发展计划(863计划)(No.2008AA8041302)、国家自然科学基金项目(No.60275013)资助

作者简介: 彭刚(通讯作者),博士,副教授,主要研究方向为机器人与智能制造系统.E-mail:penggang@hust.edu.cn.杨诗琪,硕士研究生,主要研究方向为计算机视觉、深度学习.E-mail:albert_yang@hust.edu.cn.黄心汉,学士,教授,主要研究方向为智能机器人、模式识别.E-mail:xhhuang@mail.hust.edu.cn.苏豪,硕士研究生,主要研究方向为智能机器人、深度学习.E-mail:M201472226@mail.hust.edu.cn.

引用本文:

彭刚, 杨诗琪, 黄心汉, 苏豪. 改进的基于区域卷积神经网络的微操作系统目标检测方法[J]. 模式识别与人工智能, 2018, 31(2): 142-149. PENG Gang, YANG Shiqi, HUANG Xinhan, SU Hao. Improved Object Detection Method of Micro-operating System Based on Region Convolutional Neural Network. , 2018, 31(2): 142-149.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201802005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I2/142

[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet Classification with Deep Convolutional Neural Networks // PEREIRA F, BURGES C J C, BOTTOU L, et al., eds. Advances in Neural Information Processing Systems 25. Cambridge, USA: The MIT Press, 2012: 1097-1105.
[2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 580-587.
[3] GIRSHICK R. Fast R-CNN // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1440-1448.
[4] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[C/OL]. [2017-08-21]. https://arxiv.org/pdf/1506.01497.pdf.
[5] REDMON J, DIVVALA S, GIRSHICK R, et al. You Only Look Once: Unified, Real-Time Object Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 779-788.
[6] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single Shot Multibox Detector // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 21-37.
[7] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 770-778.
[8] SHRIVASTAVA A, GUPTA A, GIRSHICK R. Training Region-Based Object Detectors with Online Hard Example Mining // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 761-769.
[9] DENG J, DONG W, SOCHER R, et al. ImageNet: A Large-Scale Hierarchical Image Database // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 248-255.
[10] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J/OL]. [2017-08-21]. https://arxiv.org/pdf/1409.1556v6.pdf.
[11] SZEGEDY C, LIU W, JIA Y Q, et al. Going Deeper with Convolutions // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015. DOI: 10.1109/CVPR.2015.7298594.
[12] IOFFE S, SZEGEDY C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[C/OL]. [2017-08-21]. https://arxiv.org/pdf/1502.03167v3.pdf.
[13] VEIT A, WILBER M J, BELONGIE S. Residual Networks Behave Like Ensembles of Relatively Shallow Networks // LEE D D, SUGIYAMA M, LUXBURG U V, et al., eds. Advances in Neural Information Processing Systems 29. Cambridge, USA: The MIT Press, 2016: 550-558.
[14] HE K M, ZHANG X Y, REN S Q, et al. Identity Mappings in Deep Residual Networks // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 630-645.
[15] UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective Search for Object Recognition. International Journal of Computer Vision, 2013, 104(2): 154-171.
[16] HE K M, ZHANG X Y, REN S Q, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 346-361.
[17] SU J, HUANG X H, WANG M. Pose Detection of Partly Covered Target in Micro-Vision System // Proc of the 10th World Congress on Intelligent Control and Automation. Washington, USA: IEEE, 2012: 4721-4725.