Focusing on the issues of poor depth estimation and inaccurate depth value acquisition under traditional machine learning methods, a depth estimation model based on Atrous Convolutional Neural Network (ACNN) was proposed. Firstly, the feature map of original image was extracted layer by layer using Convolutional Neural Network (CNN). Secondly, with the atrous convolution structure, the spatial information in original image and the extracted feature map were fused to obtain initial depth map. Finally, the Conditional Random Field (CRF) with combining three constraints, pixel spatial position, grayscale and gradient information were used to optimize initial depth map and obtain final depth map. The model usability verification and error estimation were completed on objective data set. The experimental results show that the proposed algorithm obtains lower error value and higher accuracy. The Root Mean Square Error (RMS) is averagely reduced by 30.86% compared with machine learning based algorithm, and the accuracy is improved by 14.5% compared with deep learning based algorithm. The proposed algorithm has a significant improvement in error reduction and visual effect, indicating that the model can obtain better results in image depth estimation.