CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Regression Convolutional Network for Vanishing Point Detection
Yan S(闫帅)1,2; Yan T(闫田田)1,2; G Yang(杨国栋)2; Z Liang(梁自泽)2
2017
Conference Namethe 32nd Youth Automation Conference of Chinese Association of Automation
Pages634-638
Conference Date2017/5/1
Conference Place合肥
Publication Place合肥
Funding Organization中国科技大学
Abstract
This paper presents a detection method of insulator stings for aerial inspection based on feature-fusion. The local subimages of insulator strings are firstly collected from aerial videos and tagged to establish a training dataset. The fusion feature is then composed by the histogram of oriented gradients (HOG) feature and local binary pattern (LBP) feature after the principal component analysis (PCA) dimension reduction separately. A training model is developed by SVM This paper presents a detection method for estimation of vanishing point position with designed regression
convolutional neural network. Due to the deep structures of convolutional networks, global high-level features are
extracted from the whole image, which helps to locate the vanishing point. In this paper, we provide a new structure of
regression neural network based on AlexNet. The structure consists of five convolutional layers, four fully connected
layers, Tanh activation function and regression loss function. We feed the neural net with a small number of training
dataset and the result proves that this method is adaptable. Compare to classical method, deep learning is more effective
on blurred pictures and complex circumstances. 
KeywordCnn Regression Vanishing Point Alexnet
URL查看原文
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21479
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.University of Chinese Academy of Sciences
2.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation of, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan S,Yan T,G Yang,et al. Regression Convolutional Network for Vanishing Point Detection[C]. 合肥,2017:634-638.
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