Transactions of Nanjing University of Aeronautics & Astronautics
Fast Segmentation of Solar Panels in Satellite ISAR Images Using a Multitask-YOLO Network
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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.
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This work was supported in part by the Shanghai Aerospace Science and Technology Innovation Foundation （No.SAST 2021-026），and the Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics（NUAA）.
Clc Number:
TN957.52
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