小天体导航陆标深度学习预测框匹配算法  

Deep Learning Prediction Frame Matching Algorithm of Small Celestial Navigation Landmarks

作　　者：肖扬 李帅 王光泽 邵巍[1] 姚文龙[1] XIAO Yang;LI Shuai;WANG Guangze;SHAO Wei;YAO Wenlong(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266100,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,青岛266100

出　　处：《深空探测学报（中英文）》2022年第4期400-406,共7页Journal Of Deep Space Exploration

基　　金：国家自然科学基金资助项目(61773227,61971253)。

摘　　要：深度学习算法对小天体陨石坑等导航陆标的识别率比传统算法高,但难以实现在多种图像变化下的匹配,针对此问题提出一种基于特征描述符的识别预测框描述方法,并完成识别结果的匹配。该算法首先确定识别预测框圆形支撑区域,构建具有旋转平移、尺度和亮度不变性的10维特征描述符,采用描述符向量间相对距离对预测框匹配。仿真结果表明该算法对不同变换下图像都具有较强的鲁棒性,预测框正确匹配率达到90%以上,可为小天体探测导航提供技术支持。Deep learning algorithm has a higher recognition rate for navigation landmarks such as small meteor craters than traditional algorithms,but it is difficult to achieve matching under various image changes. To solve this problem,a description method of recognition prediction box based on feature descriptor was proposed,and the matching of recognition results was completed. Firstly,the circular support region of the recognition prediction frame was determined and a 10-dimensional feature descriptor with rotation and translation scale and luminance invariance was constructed and the prediction frame was matched by the relative distance between descriptor vectors. The results show that the proposed algorithm is robust to images under different transformations,and the correct matching rate of the prediction frame is over 90%. It may provide the reference for the asteroid exploration navigation system.

关 键 词：深度学习 导航陆标 支撑区域 特征描述符 预测框匹配 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]