A Line Segment Detector for Space Target Images Robust to Complex Illumination

Author:

Zhang Xingxing1,Hu Changyu2,Liu Hanhan1,Du Ronghua3,Zhou Xiaofeng1,Wang Ling1ORCID

Affiliation:

1. Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China

2. School of Electronic Information Engineering, Wuxi University, Wuxi 214063, China

3. College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract

The relative pose estimation of the space target is indispensable for on-orbit autonomous service missions. Line segment detection is an important step in pose estimation. The traditional line segment detectors show impressive performance under sufficient illumination, while it is easy to fail under complex illumination conditions where the illumination is too bright or too dark. We propose a robust line segment detector for space applications considering the complex illumination in space environments. An improved two-dimensional histogram construction strategy is used to optimize the Otsu method to improve the accuracy of anchor map extraction. To further improve line segment detection’s effect, we introduce an aggregation method that uses the angle difference between segments, the distance between endpoints, and the overlap degree of segments to filter the aggregation candidate segments and connect disjoint line segments that probably came from the same segment. We demonstrate the performance of the proposed line segment detector using a variety of images collected on a semiphysical simulation platform. The results show that our method has better performance than traditional line segment detectors including LSD, Linelet, etc., in terms of line detection precision.

Funder

Shanghai Aerospace Science and Technology Innovation Foundation

Natural Science Foundation of Jiangsu Province

Publisher

MDPI AG

Subject

Aerospace Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Contrast-Guided Line Segment Detection;IEEE Signal Processing Letters;2024

2. CF-lines: a fusing contour features optimization method for line segment detector;The Journal of Supercomputing;2023-09-05

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