DEF-Net: A dual-modal feature enhancement and fusion network for infrared and visible object detection

Xiaoming Guo, Fengbao Yang and Linna Ji

PLOS ONE, 2026, vol. 21, issue 4, 1-24

Abstract: Infrared-visible object detection in complex dynamic environments often suffers from weak feature representation and underutilized cross-modal complementarity, leading to missed and false detections. To address these issues, we propose a Dual-modal Enhanced Feature Enhancement and Fusion Network (DEF-Net). To enhance the model’s focus on informative features within both infrared and visible modalities, a feature interaction enhancement module is designed to effectively highlight and reinforce salient information. Furthermore, to better exploit the complementary characteristics of the two modalities, a transformer-based fusion architecture incorporating a cross-attention mechanism is introduced, enabling deep inter-modal feature integration. Experiments on SYUGV and LLVIP datasets show that DEF-Net outperforms existing methods in accuracy while maintaining real-time processing speed.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0345815

DOI: 10.1371/journal.pone.0345815

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