Abstract：To address the challenges of low detection accuracy, strong background interference, and insufficient hard sample mining in complex cross-domain scenarios such as UAV aerial photography, low-light conditions, and foggy weather, a cross-domain object detection method integrating heterogeneous attention and topological knowledge diffusion(HATKD) is proposed. The method is synergistically optimized through three core modules. First, a dynamic fusion feature enhancement(DFFE) module is designed. A dual-path heterogeneous attention mechanism is employed to capture multi-granularity spatial information and multi-granularity channel information, thereby filtering highly transferable features and suppressing background noise. Second, a category-aware topological knowledge diffusion module is designed to construct a global topological structure matrix. Hamiltonian graph theory is introduced to build a category prototype memory bank. Cross-domain semantic relationships are aligned through intra-class compactness and inter-class separability constraints. Finally, a spatial-aware hard sample mining(SAHSM) module is designed to optimize the weights of hard samples through confidence-geometry-feature three-level filtering. Thus, the foreground-background imbalance problem is alleviated and the detection capability for hard samples is improved. Experimental results on four datasets demonstrate the superior performance of the proposed method, particularly in small object detection and large object detection. The feature focusing ability of the proposed method is further confirmed through visual heatmaps. Moreover, ablation experiments validate the necessity of each module.
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