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Obstacle Detection with Deep Convolutional Neural Network

机译:深度卷积神经网络的障碍物检测

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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.
机译: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。由于手工设计的功能在复杂的背景中易受攻击,因此传统的计算机视觉算法无法很好地解决此问题。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的深层架构确保网络学习到的功能丰富且有效地检测障碍物。结果表明,该模型取得了良好的性能。

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