A monocular visual depth estimation method is proposed based on deep convolutional neural networks, in which an end-to-end learning framework is used to construct a model. A residual network (ResNet) is used as the coding part of the neural network model framework to extract the depth information features. The encoded information is decoded by a densely concatenated convolution network (DenseNet). The integration of the encoded and decoded information streams is realized by Skip-Connections, which avoids the loss of inter-layer information under transmission. The experimental results show that the depth convolution neural network can be used to estimate visual depth more effectively and accurately than other monocular visual depth estimation methods.