# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 1 Introduction  
Accurate measurement of the relative pose of space targets is a prerequisite for on-orbit service missions. Vision-based measurement technology has great potential advantages in the pose estimation missions of close-range space targets. Visual pose estimation methods can be divided into traditional measurement methods and deep learning methods. There are two main types of pose estimation methods based on deep learning: based on the target recognition network and based on the pose estimation network. The pose estimation algorithm based on the target recognition network is an indirect pose estimation method; it first uses the target recognition network for feature extraction to obtain key point position information and then uses the traditional method for pose estimation [1; 2; 3]. The method based on the pose estimation network is a relatively direct pose estimation method, which directly obtains the six degree of freedom pose estimation result through the overall regression and categorical voting network [4; 5; 6; 7]. Deep learning techniques have achieved great success in many fields, especially in computer vision. However, space applications differ from ground tasks because of their high reliability requirements and the relatively limited computing resources of space hardware [8]. The space target pose estimation method based on deep learning has a large amount of computation and lacks a real large dataset, and whether it can meet the high requirements of space missions for robustness has not been verified in actual space tasks. In contrast, traditional visual pose estimation methods are more reliable and have a large number of applications in space missions,but they are greatly affected by environmental factors and need to be further studied for specific problems. For most manmade space targets, the key structures of space targets are mostly composed of line segments. Compared with other features, line segments are less affected by observation conditions such as illumination and noise. Using line segments to represent space targets can reduce the complexity of the pose estimation algorithm. At the same time, the robustness of the algorithm will be greatly improved. In addition to applications in target pose estimation [9], line segment extraction is also widely used in the fields of target detection [10; 11; 12; 13], image registration [14], target recognition [15; 16], and 3D reconstruction [17; 18]. Different from the feature extraction problem of motion targets in general scenes, the line segment extraction of noncooperative targets in space for the in-orbit control process faces more challenges. In space, (1) the illumination varies dramatically. The strong sunlight leads to high contrast and high dynamic range of the image, where the image brightness on the side facing the sun is large and even oversaturated, while the side that is not exposed to the sun is dark and the image brightness is low. How to accurately extract the target line segment features under complex illumination poses a challenge to the existing traditional line segment detection algorithms. (2) The star background, shadow, and spatial noise challenge the robustness of the line segment detection algorithm. (3) The on-orbit operation missions have higher requirements for the real-time performance of the line segment detection algorithm. The on-orbit operation missions require the spacecraft to have the ability of guidance, navigation, and control. The premise of attitude orbit control is to obtain the pose information of the target, which puts forward requirements for the speed of pose estimation and line detection algorithm. Generally, to ensure that the space on-orbit operation missions can be completed stably, the speed of the whole pose estimation algorithm needs to be guaranteed at more than 4 HZ. The existing line segment detection methods can be broadly classified into two categories: the Hough transform (HT)-based approach and the perceptual-grouping-based approach.