Detecting Resident Space Objects (RSOs) and preventing collisions with other satellites is crucial. Recently, deep convolutional neural networks (DCNNs) have shown superior performance in object detection when large-scale datasets are available. However, collecting rich data of RSOs is difficult due to very few occurrences in the space images. Without sufficient data, it is challenging to comprehensively train DCNN detectors and make them effective for detecting RSOs in space images, let alone to estimate whether a detector is sufficiently robust. The lack of meaningful evaluation of different detectors could further affect the design and application of detection methods. To tackle this issue, we propose that the space images containing RSOs can be simulated to complement the shortage of raw data for better benchmarking. Accordingly, we introduce a novel simulation-augmented benchmarking framework for RSO detection (SAB-RSOD). In our framework, by making the best use of the hardware parameters of the sensor that captures real-world space images, we first develop a high-fidelity RSO simulator that can generate various realistic space images. Then, we use this simulator to generate images that contain diversified RSOs in space and annotate them automatically. Later, we mix the synthetic images with the real-world images, obtaining around 500 images for training with only the real-world images for evaluation. Under SAB-RSOD, we can train different popular object detectors like Yolo and Faster RCNN effectively, enabling us to evaluate their performance thoroughly. The evaluation results have shown that the amount of available data and image resolution are two key factors for robust RSO detection. Moreover, if using a lower resolution for higher efficiency, we demonstrated that a simple UNet-based detection method can already access high detection accuracy.

翻译：检测驻留空间物体（RSO）并避免与其他卫星碰撞至关重要。近年来，深度卷积神经网络（DCNN）在大规模数据集可用时，在目标检测领域展现出卓越性能。然而，由于空间图像中RSO出现频率极低，收集其丰富数据十分困难。缺乏充足数据使得全面训练DCNN检测器并使其有效检测空间图像中的RSO充满挑战，更遑论评估检测器的鲁棒性。不同检测器缺乏有意义的评估会进一步影响检测方法的设计与应用。为解决这一问题，我们提出可通过模拟包含RSO的空间图像来补充原始数据短缺，以实现更优基准测试。为此，我们引入了一种新颖的模拟增强基准框架用于RSO检测（SAB-RSOD）。在该框架中，通过充分利用采集真实空间图像的传感器硬件参数，我们首先开发了一种高保真RSO模拟器，可生成多种逼真的空间图像。随后，利用该模拟器生成包含空间中多样化RSO的图像并自动标注。接着，将合成图像与真实图像混合，仅使用真实图像进行评估，获得约500张训练图像。在SAB-RSOD框架下，我们能有效训练Yolo、Faster RCNN等不同主流目标检测器，从而全面评估其性能。评估结果表明，可用数据量和图像分辨率是实现鲁棒RSO检测的两个关键因素。此外，若以较低分辨率换取更高效率，我们证明基于UNet的简单检测方法已能达到高检测精度。