DeCo: A Multimodal Detection Framework based on Density Consensus Learning

Multimodal object detection aims to exploit complementary observations from heterogeneous sensors for accurate object localization. However, existing fusion approaches mainly rely on feature-level interaction without explicitly modeling object distribution and spatial organization, limiting their performance in dense scenes. This limitation is particularly evident in remote sensing applications such as optical and SAR ship detection. In this paper, we propose DeCo, a multimodal detection framework based on density consensus learning. DeCo formulates multimodal fusion as consensus estimation over a shared object distribution space, where feature interaction is guided by cross-modal spatial agreement and density consensus, enabling robust multimodal detection under high target density. Specifically, modality-specific encoders transform optical and SAR observations into a shared latent density space, where spatially varying modality reliability is estimated to construct a cross-modal density consensus representation. The learned consensus subsequently regulates multiscale feature fusion by adaptively allocating modality contributions and enhancing spatially consistent object evidence. To supervise density learning without manual density profile design, we introduce a box-aware Bayesian density objective that optimizes instance-level mass assignment through probabilistic ownership modeling. Extensive experiments on paired optical–SAR ship detection benchmarks demonstrate that DeCo achieves robust multimodal detection and accurate object counting, especially in densely packed scenes.