Target-oriented deformable fast depth estimation based on stereo vision for space object detection

Chengcheng Xu, Haiyan Zhao, Bingzhao Gao, Hangyu Liu, Hongbin Xie

2024/12/29

Measurement

Volume 245, Pages 116621, Elsevier

Abstract：

To address problems of the poor matching accuracy and speed in space object detection, this paper proposes a deformable fast object matching algorithm. It is a target-oriented depth estimation approach that calculates the disparity value by matching the object on the left and right images. The logical encoding masking layer is designed to achieve the deformable operation, which can fully fuse the semantic or contour feature information of the object. This effectively reduces the computational cost and improves the accuracy. The feature coding method is optimized and upgraded by integrating relative positions and global pixels in the image, solving the problem of mismatching in complex regions without obvious features. Based on the characteristics of stereo vision and the concept of regional matching, an optimal matching search range is proposed. Results show that the average time is less than 9ms and accuracy reaches the state-of-the-art.

Cite：

@article{XU2025116621,
title = {Target-oriented deformable fast depth estimation based on stereo vision for space object detection},
journal = {Measurement},
volume = {245},
pages = {116621},
year = {2025},
issn = {0263-2241},
doi = {https://doi.org/10.1016/j.measurement.2024.116621},
url = {https://www.sciencedirect.com/science/article/pii/S0263224124025065},
author = {Chengcheng Xu and Haiyan Zhao and Bingzhao Gao and Hangyu Liu and Hongbin Xie}}