Abstract: With the rapid development of artificial intelligence, object detection and recognition have become increasingly important. Deep learning-based object detection techniques that fuse visible and infrared images demonstrate robust feature extraction and generalization capabilities, effectively integrating features from both modalities. This paper first reviews the current state of dual-modal image fusion for object detection. It then analyzes the advantages of dual-modal fusion within deep learning-based detection and compares commonly used datasets and key technical challenges. Next, the paper summarizes object detection algorithms based on different fusion stages, emphasizing the benefits and dominance of feature-level fusion. It further analyzes fusion detection algorithms based on different base models, highlighting the advantages and dominant role of the Transformer and the potential of Mamba for future research. Finally, the paper provides a forward-looking perspective on future research oriented towards practical applications.

Key words: deep learning, object detection, dual-modal fusion detection, image fusion

摘要： 随着人工智能技术的快速发展，目标检测与识别的地位日益凸显。基于深度学习的可见光-红外图像融合的目标检测技术具有强大的特征提取和泛化能力，能够有效提取和融合可见光与红外图像特征。对基于双模态图像融合检测的发展现状进行概述，并在基于深度学习的目标检测基础上分析双模态图像融合检测的优势，对比介绍常用的数据集和主要的技术难题。对基于不同阶段融合的目标检测算法进行总结分析，指出特征级融合检测的优势与主导地位；重点对基于不同基础模型的融合检测算法进行分析和总结，探讨了Transformer在双模态融合检测领域的优势和主导地位，以及Mamba在未来研究中的巨大潜力。根据当前可见光-红外图像融合的目标检测研究现状，对未来以实际的开发应用为导向进行了展望。

关键词: 深度学习, 目标检测, 双模态融合检测, 图像融合