Multi path attention and scale aware fusion for accurate object detection in remote sensing imagery
Jing Liu, Junjie Tao, Xiaoyong Liu, Jun Ma, Chaoping Guo, Chunyu Dong, Pan Li, Peijun Shi · Scientific Reports · 2025
The pursuit of accurate yet computationally efficient object detection within remote sensing imagery remains a cornerstone for the advancement of intelligent interpretation systems. Although substantial progress has been achieved in recent years, prevailing approaches still exhibit notable deficiencies in three critical aspects: the discriminative capacity of feature representation, the depth of semantic modeling, and the effectiveness of multi-scale information fusion. These shortcomings become particularly pronounced when addressing small-scale targets, which are highly susceptible to omission or misclassification. In response to these limitations, this work introduces HyperFusion-DEIM, a cascaded detection paradigm specifically designed to simultaneously reinforce object-level representations, enrich contextual semantic dependencies, and optimize scale-aware feature integration. Central to this framework is the Multi-Path Attention Network (MAPNet), which augments shallow semantic cues and edge-texture sensitivity for small object recognition through the joint operation of the Multi-Path Attention Fusion (MPAF) module and the Shallow Robust Feature Downsampling (SRFD) mechanism. Complementing this, the Scale-Aware Feature Enhancement (SAFE) encoder incorporates a Multi-level Feature Concentration (MFC) module to achieve cross-layer geometric alignment, while the integration of Transformer layers with HyperACE enables the capture of long-range semantic correlations without compromising spatial fidelity. Empirical validation conducted on the SIMD and VEDAI benchmarks demonstrates the clear superiority of HyperFusion-DEIM over state-of-the-art lightweight detectors in both predictive accuracy and robustness. Specifically, the model attains 64.5% AP on SIMD, outperforming RT-DETR and DEIM by 4.8% and 4.6%, respectively, while sustaining a peak inference throughput of 296.33 FPS. On VEDAI, HyperFusion-DEIM surpasses YOLOv12 and YOLOv13 by margins of 4.9% and 8.0%, and exceeds RT-DETRv2 and DEIM by 2.5% and 8.5%, all while maintaining real-time operation at 79.7 FPS. This performance showcases HyperFusion-DEIM's practical viability for real-time detection, particularly in resource-constrained environments where both speed and accuracy are critical.