Detection of Parabolic Antennas in Satellite Inverse Synthetic Aperture Radar Images Using Component Prior and Improved-YOLOv8 Network in Terahertz Regime

Inverse Synthetic Aperture Radar (ISAR) images of space targets and their key components are very important. However, this method suffers from numerous drawbacks, including a low Signal-to-Noise Ratio (SNR), blurred edges, significant variations in scattering intensity, and limited data availability, all of which constrain its recognition capabilities. The terahertz (THz) regime has reflected excellent capacity for space detection in terms of showing the details of target structures. However, in ISAR images, as the observation aperture moves, the imaging features of the extended structures (ESs) undergo significant changes, posing challenges to the subsequent recognition performance. In this paper, a parabolic antenna is taken as the research object. An innovative approach for identifying this component is proposed by using the advantages of the Component Prior and Imaging Characteristics (CPICs) effectively. In order to tackle the challenges associated with component identification in satellite ISAR imagery, this study employs the Improved-YOLOv8 model, which was developed by incorporating the YOLOv8 algorithm, an adaptive detection head known as the Dynamic head (Dyhead) that utilizes an attention mechanism, and a regression box loss function called Wise Intersection over Union (WIoU), which addresses the issue of varying sample difficulty. After being trained on the simulated dataset, the model demonstrated a considerable enhancement in detection accuracy over the five base models, reaching an mAP50 of 0.935 and an mAP50-95 of 0.520. Compared with YOLOv8n, it improved by 0.192 and 0.076 in mAP50 and mAP50-95, respectively. Ultimately, the effectiveness of the suggested method is confirmed through the execution of comprehensive simulations and anechoic chamber tests.