Depth estimation based on unsupervised learning is one of the important issues in the field of computer vision. However， existing algorithms of depth estimation are mainly designed based on visible images. Compared with visible images， thermal infrared images have the disadvantages of low contrast and insufficient detailed information. To this end， a depth estimation network is constructed and an unsupervised depth estimation method is proposed for thermal infrared images according to their characteristics. The network consists of three parts： feature extraction module， feature aggregation module， and feature fusion module. Firstly， a feature aggregation module is designed to improve network ability to acquire the edge information of target objects and the small object information of the image. Secondly， the channel attention mechanism is introduced in feature fusion module to effectively capture the interaction relationship between different channels. Finally， a depth estimation network for thermal infrared images is established. In this network， the model parameters are trained by thermal infrared sequence images to achieve the pixel-level depth estimation of a single thermal infrared image. The results of ablation studies and comparative experiments fully demonstrate that the performance of the proposed method in pixel-level depth estimation of thermal infrared image outperforms other representative methods.