## 3 Improved Network for Component Detection
### Structure of Improved-YOLOv8 model  
The most recent addition to the YOLO family of target detection algorithms, YOLOv8, boasts improvements across several key components over its earlier iterations. YOLOv8 incorporates an anchor mechanism inspired by YOLOX [30], which offers benefits in dealing with objects of elongated, unconventional shapes. Additionally, for the purpose of loss calculation in positive sample matching, YOLOv8 utilizes an adaptive multi-positive sample matching technique. A variety of internal and external factors often result in size variations for satellites and their components, even in regions that appear identical. This diversity affects the size of the target detection area for the parabolic antenna. Yet, the target detection network generates detection boxes based on the fixed dimensions of a predefined box, which lacks flexibility. Further analysis of the samples in the dataset reveals that most samples have clear features and can be readily identified as simple samples. In contrast, a smaller number of samples have indistinct features, which raises the risk of misclassification, and are labeled as difficult samples. This creates a notable imbalance between simple and difficult samples. Nonetheless, it is essential for the model to be trained to recognize and differentiate these difficult samples so that the network's detection capabilities can be improved. Furthermore, the research introduces a refined algorithm derived from YOLOv8, designed for the automated detection of targets across multiple scales. Initially, a Dyhead [31] module, which employs an attention mechanism, is integrated into the algorithm's head  
Figure 6: Imaging results and corresponding CAD under three typical observation apertures. component to enable flexible detection of parabolic antenna targets that vary in size. Then, within the Intersection over Union (IoU) loss component, the standard Complete Intersection over Union (CIoU) from the YOLOv8 model is replaced with WIoU [32] to tackle the issue of sample imbalance between those that are easy to detect and those that are challenging. Figure 7 illustrates the primary architecture of the Enhanced-YOLOv8 algorithm. (1) Dyhead: The attention mechanism is a sophisticated processing strategy that prioritizes relevant information while excluding irrelevant, extraneous data. It adeptly captures the relative importance of various semantic levels and selectively boosts features in alignment with the dimensions of an individual object [31]. The Dyhead module integrates the target detection head and the attention mechanism together. This methodology seamlessly converges several self-attention components across different feature layers, spatial positions, and output channels, facilitating the concurrent acquisition of scale, spatial, and task awareness within a cohesive target detection architecture. This integration remarkably promotes the representational power of the detection head without augmenting the computational complexity. As illustrated in Figure 8, the detection head receives a scaled feature pyramid F, which is represented as a tensor \(F\in R^{L\times S\times C}\), where L represents the number of layers within the pyramid. \(S=H\times W\); \(H\) and \(W\) represent the height and width. And \(C\) denotes channel number of the median horizontal feature. The general procedure for utilizing self-attention can be expressed as:  
\[W(\mathcal{F})=\pi(\mathcal{F})\cdot\mathcal{F} \tag{21}\]  
where \(\pi(\cdot)\) denotes an attention mechanism. A straightforward approach to addressing this attention mechanism involves the use of fully connected neural networks. However, training the attention function across all dimensions is computationally intensive and, in practice, not feasible due to the tensor's high dimensionality.