Running the code: DEF-Net.

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.复杂动态环境下的红外可见光目标检测常面临特征表征能力薄弱、跨模态互补性未充分利用的问题，进而导致漏检与误检现象。为解决上述问题，本文提出双模态增强特征增强与融合网络（Dual-modal Enhanced Feature Enhancement and Fusion Network，DEF-Net）。为提升模型对红外与可见光模态中有效特征的聚焦能力，本文设计了特征交互增强模块，可有效凸显并强化显著信息。此外，为更好地利用两种模态的互补特性，本文引入了融合跨注意力机制的基于Transformer的融合架构，实现深度跨模态特征融合。在SYUGV与LLVIP数据集上的实验结果表明，DEF-Net在保持实时处理速度的同时，检测精度优于现有方法。