Dual Causal-Aware Detection Transformer for Remote Sensing Images

Deep neural networks often inherit biases from training data, compromising generalization. In visual recognition, distinguishing foreground from background is insufficient, as models tend to rely on spurious correlations rather than learning essential causal patterns. To address this issue, this paper proposes a novel transformer architecture, termed dual causal-aware detection transformer (DCDT), specifically designed for object detection in optical remote sensing images from a causal perspective. Specifically, we begin by constructing a structural causal model to intuitively analyze the causal effects inherent in the overall visual patterns. Building on this foundation, DCDT introduces causal constraints at the attention level by embedding dynamic multi-scale causal prototypes into the attention mechanism. The derived causal priors are subsequently used to enhance features at the representation level, thereby enforcing feature-level causal modulation. This dual causal-aware strategy enables the precise extraction and reinforcement of causally relevant features, improving both robustness and discriminative capability in complex detection scenarios. In addition, a sparse kernel-region mask is incorporated to decouple local information from global representations, effectively strengthening the modeling of fine-grained structures. Extensive experiments conducted on two challenging public datasets, DIOR and HRRSD, demonstrate that DCDT consistently outperforms existing methods and baselines. These results validate the effectiveness of DCDT in capturing both global causal semantics and local fine-grained features, highlighting its practicality in complex remote sensing scenarios.