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
会议名称the 32nd Youth Automation Conference of Chinese Association of Automation
页码634-638
会议日期2017/5/1
会议地点合肥
出版地合肥
会议主办者中国科技大学
摘要
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. 
关键词Cnn Regression Vanishing Point Alexnet
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收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/21479
专题复杂系统管理与控制国家重点实验室_先进机器人
作者单位1.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
推荐引用方式
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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