南京航空航天大学学报（英文版）2024，Vol.41Issue(2)：253-262,10.DOI:10.16356/j.1005⁃1120.2024.02.010
基于Multitask-YOLO网络的卫星帆板ISAR图像快速分割
Fast Segmentation of Solar Panels in Satellite ISAR Images Using a Multitask-YOLO Network
摘要
Abstract
With the rapid development of space technology,the situation awareness ability of spacecraft is increased.As compared to the optical sensors,inverse synthetic aperture radars(ISARs)have the capability of high-resolution imaging in all day from far range regardless of the light condition.Furthermore,the component recognition is much desired by the accurate evaluation of the threat degree of surrounding spacecrafts.In this paper,we propose a multitask-you only look once(Multitask-YOLO)network based on the YOLOv5 structure for recognition and segmentation of solar panels of satellite ISAR images.Firstly,we add a segmentation decoupling head to introduce the function of segmentation.Then,the original structure is replaced with spatial pyramid pooling fast(SPPF)to avoid image distortion,and with distance intersection over union(DIoU)to speed up convergence.The accuracy of segmentation and recognition is improved by introducing an attention mechanism in the channels.We perform the experiments using simulated satellite ISAR images.The results show that the proposed Multitask-YOLO network achieves efficient and accurate component recognition and segmentation.As compared to typical recognition and segmentation networks,the proposed network exhibits an approximate 5%improvement in mean average precision(mAP)and mean intersection over union(mIoU).Moreover,it operates at a higher speed of 16.4 GFLOP,surpassing the performance of traditional multitask networks.关键词
Multitask-YOLO/空间目标/逆合成孔径雷达图像/目标识别与分割Key words
Multitask-YOLO/space objects/inverse synthetic aperture radar(ISAR)images/target recognition and segmentation分类
信息技术与安全科学引用本文复制引用
姚雨晴,汪玲,王莲子,张弓,吴斌,朱岱寅..基于Multitask-YOLO网络的卫星帆板ISAR图像快速分割[J].南京航空航天大学学报（英文版）,2024,41(2):253-262,10.基金项目
This work was supported in part by the Shanghai Aerospace Science and Technology Innovation Foundation(No.SAST 2021-026),and the Fund of Prospec-tive Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics(NUAA). （No.SAST 2021-026）
