## 1 Introduction
The basic scattering characteristics and imaging methods for the terahertz frequency band are thoroughly discussed in reference [12]. This study demonstrates that the number of scattering centers in a scene increases significantly with the presence of rough surfaces, which can degrade image quality. To address the image quality degradation caused by dense scattering centers, researchers have investigated terahertz imaging enhancement techniques based on sparse regularization [13] and machine learning [14]. These methods have been shown to effectively surpass the Rayleigh limit, which is a fundamental limit of resolution in traditional imaging techniques. When these imaging enhancement methods were first proposed, scholars aimed to achieve a level of detail in the object of interest that was as clear as optical images. This goal was driven by the desire to obtain highly resolved and detailed images, even in the presence of complex scattering environments that traditionally limit the clarity of terahertz images. Through our research, due to the special azimuth dependence of extended structures (ESs), the key extended structures of complex targets such as parabolic antennas may disappear and be discretized into specular points, edge pair-points, or ellipse arcs, which are quite different from optical images. Therefore, we need to propose a new image interpretation and translation method for parabolic antennas. The Constant False Alarm Rate (CFAR) detection technique, a well-established method, is widely utilized for its straightforwardness and efficiency [15]. A robust CFAR detector, grounded in truncated maximum likelihood principles, is adeptly implemented in environments prone to outlier interference, demonstrating adequate computational efficiency [16]. Despite this, contemporary object detection approaches leveraging deep neural networks have outperformed traditional methods in both accuracy and speed [17; 18; 19; 20]. [PERSON] et al. [21] developed two alternative systems that utilize the Single-Shot multibox Detector (SSD) and the Tiny YOLOv2 network for the automated detection of sinking funnels generated by underground coal mining. A method integrating Interferometric Synthetic Aperture Radar (InSAR) and Convolutional Neural Networks (CNNs) is proposed for the automatic identification of subsidence funnels caused by coal mining activities [22]. [PERSON] et al. [23] introduced a lean model named Light You Only Look Once (YOLO)-Basin, which is designed for subsidence basin detection using the YOLOv5 network. Under the premise of key frames, a technology to estimate the parameters of a parabolic antenna is presented explicitly [24]. This method significantly reduces the computational complexity of the model and improves the accuracy of the model. In the terahertz frequency ban, by analyzing the sliding scattering center and cross-polarization imaging characteristics of parabolic antennas, an improved Hough transform method has been proposed for identifying parabolic antennas [25]. Through the analysis of the prior information of the components and their imaging characteristics, the literature effectively identifies the components and is able to successfully reconstruct them in the terahertz regime [26]. These advancements offer valuable insights that inform the development of our proposed approach. In this article, a novel method for detecting parabolic antennas is proposed. The main idea lies in the good use of CPICs as well as an improved-YOLOv8 object detection network. To make full use of the CPICs of the component, four objects are to be detected: the satellite body and the three statuses of the parabolic antenna, namely, specular point, edge-pair points, and ellipse arc. We not only give the characteristics of the parabolic antenna when imaging it alone but also realize the identification of parabolic antenna based on the detection of satellite.