To solve the problem of obtaining a higher accuracy at the expense of redundant models, we propose a network architecture. We utilize a lightweight network that retains the high-precision advantage of the transformer and effectively combines it with convolutional neural network. By greatly reducing the training parameters, this approach achieves high precision, making it well suited for deployment on edge devices. A detail highlight module (DHM) is added to effectively fuse information from multiple scales, making the depth of prediction more accurate and clearer. A dense geometric constraints module is introduced to recover accurate scale factors in autonomous driving without additional sensors. Experimental results demonstrate that our model improves the accuracy from 98.1% to 98.3% compared with Monodepth2, and the model parameters are reduced by about 80%.