Unsupervised depth estimation for ship target based on single view UAV image

ABSTRACT

The goal of depth estimation is to obtain the depth image which reflects the distance between the object and the camera point. Depth image can provide depth information for tasks, such as three-dimensional (3D) reconstruction and distance perception. With the development of the environment perception research of the unmanned ship and unmanned aerial vehicle (UAV), depth estimation has been widely used in the field of water transportation. However, the research of ship depth estimation based on monocular images has just started, and there is no unsupervised deep learning depth estimation method for ship target based on single view UAV image. This paper proposes an unsupervised deep learning ship depth estimation method based on single view UAV images. Firstly, the realistic rendering software is used to construct a new ship training dataset with simple backgrounds and regular lighting conditions. Secondly, based on the differentiable rendering framework, a knowledge distillation depth estimation network is designed to train a student network with much smaller number of model parameters. Finally, the ship depth estimation network is obtained through unsupervised training. The experiment results show that the designed knowledge distillation depth estimation network can generate better depth estimation results compared with the current state-of-the-art (SOTA) method, and the weight file size of our model is smaller.

KEYWORDS:

• Depth estimation
• lightweight model
• ship surveillance
• unsupervised learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author [Zhengling Lei], upon reasonable request.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 52071201 and Grant 61602426, in part by the Special Funding for the Development of Science and Technology of Shanghai Ocean University under Grant A2-2006-21-200207, in part by the Fund of Hubei Key Laboratory of Inland Shipping Technology under Grant NHHY2019001, in part by the Open Project Program of the State Key Lab of CAD&CG (Zhejiang University) under Grant A2107 and in part by the Open Subject of the State Key Laboratory of Engines (Tianjin University) under Grant No.K2019-14; Open Project Program of the State Key Lab of CAD&CG (Zhejiang University) [A2107]; Open Subject of the State Key Laboratory of Engines (Tianjin University) [No.K2019-14]