Obstacle Detection with Deep Convolutional Neural Network

Hong Yu, Ruxia Hong, XiaoLei Huang, Zhengyou Wang
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引用次数: 13

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.
基于深度卷积神经网络的障碍物检测
障碍物检测的难点在于如何从复杂的背景中定位和分离障碍物。传统的计算机视觉算法不能很好地处理这一问题,因为手工设计的特征在复杂的背景下容易受到攻击。在本文中,我们使用深度卷积神经网络(CNN)来检测复杂场景中的障碍物。CNN的深度结构保证了网络学习到的特征丰富而有效地用于障碍物检测。结果表明,该模型取得了良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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