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DBF-YOLO: a lightweight dual-branch fusion method for Visible-SAR cross-modal remote sensing target detection
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OA
AI
X
C
H
王
张
H
Z
DOI：10.3389/frsen.2026.1919576.png)
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Abstract
En 中文
In visible-SAR cross-modal remote sensing target detection tasks; due to the significant differences between the two modalities in imaging mechanisms and feature representations; as well as the susceptibility of SAR images to speckle noise interference and the inadequate utilization of structural information; existing methods often struggle to balance detection accuracy with model lightweighting and practical deployment requirements. This is particularly true in resource-constrained scenarios; such as space-borne or airborne platforms; where models need to have low parameter counts; reduced computational overhead; and robust adaptability to complex environments. To address these issues; this paper proposes a lightweight dual-branch fusion network; DBF-YOLO; for visible-SAR cross-modal remote sensing target detection. Based on the YOLOv10 framework; the method constructs a visible-SAR dual-branch feature extraction structure and designs an intermediate cross-modal fusion path at three levels (P3; P4; P5) to achieve progressive interaction of dual-modal features at different scales. To overcome the strong speckle noise and edge degradation in SAR images; a SAR Gradient Enhancement Module (SGM) is introduced to enhance the structural representation capability of SAR inputs. Additionally; an Adaptive Gated Dual-Modal Fusion Module (AGD) is proposed to enable dynamic selection and effective complementarity of dual-modal information based on different scales and spatial positions. Experimental results on the OGSOD 1.0 dataset show that DBF-YOLO achieves 94.4% mAP50% and 70.1% mAP50-95; with only 5.0 M parameters and 20.5 GFLOPs; striking a good balance between detection accuracy and computational complexity. Furthermore; experimental results on the OSPRC dataset demonstrate the robustness of DBF-YOLO under different resolutions; polarization modes; and cloud cover conditions. Particularly in low-contrast dense target scenes and cloud cover conditions; the model demonstrates stronger environmental adaptability. This method can provide a reference for designing lightweight multi-modal remote sensing target detection models and deploying them on resource-limited platforms.
Keywords:
remote sensing
object detection
YOLOv10
multimodal learning
lightweight neural networks
DATA FUSION
visible-SAR fusion
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F
IF:
3.7
Papers:
560
Citations:
993
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