关键词: 深度估计, 卷积神经网络, 语义分割, 无人驾驶

Abstract:

In order to use the convolutional neural network to learn to estimate the depth of a street image, the method of semantic segmentation is proposed to solve the problem of boundary blurring of the depth map. The estimated depth is generated by the left and right perspective maps to generate disparity maps for unsupervised training. The semantic segmentation layer is added in the network model, and multiple atrous convolutions parallel structure is adopted to increase the receptive field, the number of image down sampling is reduced, the information loss during down sampling is decreased, and the result is more accurate. This is also the first time in depth estimation combined with cavity convolution to increase the accuracy. By training the KITTI street dataset, in addition to increasing the detection accuracy and reducing the error rate, the objects in the renderings are made clearer, and some of the original models are retained in the renderings, it ignores the details and shows the original image more completely.

Key words: depth estimation, convolutional neural network, semantic segmentation, self-driving