基于卷积神经网络的深度线段分类算法

为解决单目图像中冗余像素点不利于深度神经网络快速完成深度信息检测的问题,提出一种基于卷积神经网络的深度线段分类算法。对NYU-Depth数据集使用线段检测算法进行线段检测得到原始图像的线段特征图,通过数据预处理结合深度数据得到表征深度信息的线段集合及其标签,提出适用于线段特征的卷积神经网络,实现单目图像中深度线段的分类。通过在不同线段数目上进行多次多组对比试验,深度线段分类准确率达到73.50%。试验结果证明了利用卷积神经网络实现深度线段分类的可实施性,有助于更好的利用图像几何特征解决深度估计问题。

Depth segment classification algorithm based on convolutional neural network

In order to solve the problem that redundant pixels in monocular images influenced depth information detection, a depth segment classification algorithm based on the convolutional neural network was proposed. We used NYU-Depth dataset to detect the segment-based features. Afterward, depth information was represented by line segments and its labels by the data preprocessing. The convolutional neural network was designed for considering the characteristics of the segments, and the classification of depth segments in monocular images was realized. By conducting several multi-group comparison experiments on different hyper-parameters, the accuracy of depth segment classification reached 73.50%. This experimental results proved the implement ability of the depth segment classification based on convolutional neural network, which was helpful to deep estimation using geometric features of images.