Single Image Based Depth Estimation for Maritime Surface Targets

Abstract

When the intelligent water cannon strikes a surface target, it needs to know the distance to the strike target and automatically adjust the strike angle to complete the accurate strike mission. Based on this estimation, the control system of the water cannon would automatically achieve the strike mission. For a universal usage, a monocular image depth estimation method based on SC-SfMLearner is used, which first estimates the depth information of the image from one sample of the real-time video frames and then uses a polynomial fitting model to transfer a depth map into the physical distance in the real world. The experimental results show that the mean square deviation of the predicted distance results in the practical environment for shore-side water targets is between 0.02 and 0.03, and the accuracy rate is above 95 %, which is a good prediction and effectively addresses the accuracy of striking water targets in practical applications.