DACE-Det: a synergistic fusion framework of multi-task streams for object detection in anisotropically degraded imagery

Rotating Synthetic Aperture (RSA) imaging technology has the advantage of lightweight design and no requirement for on-orbit assembly. It offers an innovative solution to overcome the physical limitations of traditional large-aperture optical systems for high-resolution, high-orbit Earth observation. However, the unique, anisotropic degradation inherent to its rectangular primary mirror poses a severe challenge for automated target interpretation. Existing object detection methods lack the perceptual and adaptive capabilities to handle this directional degradation, resulting in poor performance in the RSA system. To address this, we propose a novel end-to-end Degradation-Aware and Collaboratively Enhanced Detector (DACE-Det), tailored for object detection in anisotropically degraded images. The framework first employs the Latent Degradation Pattern Embedding (LDPE) module, based on contrastive learning, to perceive and disentangle the degradation prior from the image content. The prior then guides the Hierarchical Anisotropic Feature Learning Network (HAFNet), a backbone specifically designed to extract multi-level features from anisotropically degraded images. Concurrently, the parallel restoration branch generates a detail-rich feature stream that complements the backbone features. Furthermore, the Synergistic Expert Fusion Module (SEFM) leverages a Mixture-of-Experts mechanism to dynamically fuse detection and restoration feature streams. To support research in related fields, we construct RSA-Aircraft, the first object detection dataset with anisotropic degradation, based on a full-link imaging simulation model. The dataset contains 35,704 annotated degraded images of military and civilian airports from over 100 countries and regions worldwide, captured across various years, seasons, and weather conditions. Results from both digital simulations and semi-physical experiments demonstrate that DACE-Det significantly surpasses 27 mainstream methods and outperforms the baseline by 9.86 percentage points. It offers a practical solution for object detection in RSA imagery. The RSA-Aircraft dataset will be available at https://github.com/sgtojd/RSA-Aircraft-A-benchmark-Dataset-for-Object-Detection-with-Anisotropic-Degradation for research purposes.