M2Former: Enhancing Event-Based RT-DETR for Robust and Lightweight Space Object Detection

Ruitao Pan, Chenxi Wang, Bing Han, Zhi Zhai, Xuefeng Chen

Abstract: With increasing human space activities, detecting resident space objects (RSOs) has become critical for space monitoring and on-obrit missions. Traditional optical sensors struggle in space environments due to extreme illumination variations and motion blur. Event cameras, bio-inspired sensors that asynchronously record per-pixel brightness changes, offer high temporal resolution, wide dynamic range, and low power consumption, making them promising for orbital sensing yet underexplored in this context. In this work, we present the first systematic study of event-based space object detection. To address the scarcity of event data, we construct E-SPARK, a large-scale dataset generated with affine transformations and advanced simulators. Building upon this dataset, we propose M2Former, a lightweight multi-scale MetaFormer backbone, together with an area-aware loss (AAL) tailored for small object detection. These are integrated into the RT-DETR framework, a Transformer-based detector known for its robustness but higher computational cost compared to YOLO models. Our design reduces parameters and complexity by over 50% while maintaining comparable detection accuracy. In addition, we design an improved data augmentation strategy that enriches supervision density and data diversity, further boosting detection performance. Experiments on both synthetic and real event data demonstrate that our method achieves state-of-the-art performance and strong generalization. These results highlight the potential of event cameras as a reliable sensing modality for spaceborne detection. The dataset, code, and supplementary materials are publicly available at: https://iamie-vision.github.io/M2Former/