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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location :
Hangzhou
DOI :
10.1109/ISCID.2013.73