DocumentCode
1986571
Title
Obstacle Detection with Deep Convolutional Neural Network
Author
Hong Yu ; Ruxia Hong ; Xiaolei Huang ; Zhengyou Wang
Author_Institution
Dept. of Inf. Sci., Nanchang Teachers Coll., Nanchang, China
Volume
1
fYear
2013
fDate
28-29 Oct. 2013
Firstpage
265
Lastpage
268
Abstract
The difficulty of obstacle detection is how to locate and separate the obstacle from the complex background. Traditional computer vision algorithms can not handle this problem very well due to the handcrafted designed features are vulnerable in complex background. In this article, we use deep convolutional neural network (CNN) to detect obstacle in complex scene. The deep architecture of the CNN guarantees the features learned by the network are rich and effective for detecting the obstacle. The results show that the model achieved a good performance.
Keywords
collision avoidance; computer vision; feature extraction; neural nets; object detection; CNN; complex background; computer vision algorithms; deep convolutional neural network; handcrafted designed features; obstacle detection; obstacle separation; Accuracy; Biological neural networks; Feature extraction; Image color analysis; Image edge detection; Laser radar; convolutional neural network; deep architecture; obstacle detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location
Hangzhou
Type
conf
DOI
10.1109/ISCID.2013.73
Filename
6804986
Link To Document