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Depth estimation is an important part in scene analysis, mainly composed of senor acquisition and image processing. Sensor technology requires demanding environment, thus image processing is a more general approach. The traditional way is using binocular stereo calibration to obtain depth information by geometric calculations, but it is still limited by environment factors. Therefore, as the closest approach to the actual situation, depth estimation form single monocular images has great research value. Consequently, a DensetNet based method is proposed,the method use multi-scale convolutional neural networks to acquire global features and local features. At the same time, it joins DenseNet structure for strong features propagation、features reuse to optimize features gathering. The experiments on NYU Depth V2 dataset demonstrate the effectiveness of this method. The average relative error of the prediction of this method is 0.119, the root mean squared error is 0.547, and the average log₁₀ error is 0.052.