# Intelligent Space Object Detection Driven by Data from Space Objects
## 4 Experimental Results and Analysis  
The experiment conducted in this study involves a dataset comprising 17,942 images, including 500 real data images. The size of the input image is 640 \(\times\) 640. For random allocation, the training-test split ratio is set to 8:2. The experiment was conducted on Ubuntu 18.04, using two NVIDIA A5000 GPUs (NVIDIA Corporation, Santa Clara, CA, USA), each of which has a VRAM of 24 GB, an AMD 7371 CPU (Advanced Micro Devices, Inc. Santa Clara, CA, USA), and a RAM of 56 GB. A pre-trained remote sensing plane model was taken as the initial model. During training, each batch size was set to 30, and training was performed for 300 epochs. Initially set to 0.01, the learning rate was gradually reduced to 0.001 under a cosine decay strategy. In the optimization algorithm, the Adam optimizer with a momentum parameter of 0.937 and a weight decay of 0.0005 was used. The loss function relied on CIOU loss [43] for object localization and on cross-entropy loss for classification. Data enhancement involved various operations such as Mosaic image stitching, image translation, scaling, distortion, flipping, and the random adjustment of hue, saturation, and brightness. Additionally, the MixUp image blending technique was applied to enhance the diversity of data. To improve the efficiency and robustness of training, multi-scale training and rectangular box inference techniques were applied. In this way, the high accuracy of training was achieved, as shown in Table 2. In this experiment, the single-stage object detection models from the YOLO series were adopted, all of which were of the same parameter scale. According to the experimental results, all the models from the YOLO series achieved an accuracy of over 0.92 on the space object dataset, with 0.983 as the maximum. Concerning the accuracy achieved for each component, the \"antenna-rod\" performed best in YOLOv7-R, reaching 0.993. By contrast, the \"optical-load\" performed worst in YOLOv3, despite reaching 0.892. After the introduction of RepPoint, a significant improvement of accuracy was achieved. Compared to YOLOv7, the YOLOv7-R enhanced with RepPoint was improved by 1.9 percentage in accuracy. The detection results are presented in Figure 10, which shows the comparison in accuracy among different models. With ResNet53 as the backbone network, YOLOv3 performs worse in feature extraction compared to other advanced models, achieving an overall accuracy of 0.927. By using the ELAN module for feature extraction, YOLOv7 achieved a higher accuracy of 0.964. The ELAN module, which restricts the longest and shortest paths, enables the network to learn semantic information in more depth and accelerate convergence. Enhanced with the RepPoint module, YOLOv7-R achieved the best training results, reaching an accuracy of 0.983. This is attributed to the introduction of the RepPoint module, which improved the performance of the backbone network in extracting feature information from satellite images. RepPoint enables precise localization at the semantically meaningful positions, allowing for the refined localization. Also, the extraction of object features was improved in the process of subsequent detection. Given the fact that object detection feedback does not necessarily promote the object detection represented by bounding boxes, the advantages of RepPoint in flexible object representation are confirmed. Figure 9: RepPoints represents the spatial range and semantically important local regions of the objects through a set of points. This representation is achieved through the weak localization supervision of rectangular ground-truth boxes and implicit recognition feedback. RepPoints provides the richer information that enables the more accurate description of the shape and location of objects. Figure 10: Part of the results indicating the detection performance of YOLOv7-R on the validation set and real data.