SD-DETR: Space Debris Detection Transformer Based on Dynamic Convolutional Network and Cross-Scale Collaborative Attention

With the advancement of human exploration of near-Earth space, the number of space debris in Earth's orbit has increased dramatically, posing serious threats to orbiting spacecraft. Traditional space debris detection methods exhibit significant limitations in handling small-scale targets, complex backgrounds, and real-time requirements. This paper proposes the Space Debris Detection Transformer (SD-DETR) algorithm, an end-to-end detector specifically optimized for space debris detection tasks. First, we design a Dynamic Convolutional Network backbone that enables adaptive feature extraction for multi-scale, irregularly shaped debris through the dynamic convolution mixing mechanism of the Adaptive Multi-scale Feature Block. Second, we propose a Cross-scale Collaborative Attention Fusion Module that realizes deep feature fusion through hybrid domain feature enhancement and channel-guided spatial attention mechanisms. Finally, we introduce a Multi-Level Adaptive Feature Bridging module that enhances cross-hierarchical feature integration through dual-scale spatial perception attention. On the constructed Nebula space debris dataset, SD-DETR achieves 89.4% mAP50 and 75.4% mAP50-95, improving by 2.1% and 2.9% respectively compared to the baseline model. Generalization experiments on the NCSTP-10000 dataset further validate the effectiveness of the method, demonstrating its superior detection performance in complex space environments.