电讯技术2026，Vol.66Issue(8)：1308-1316,9.DOI:10.20079/j.issn.1001-893x.250327004
融合卷积与自注意力机制的可见光-红外目标检测
Visible-Infrared Object Detection Integrating Convolution and Self-attention Mechanism
摘要
Abstract
In order to improve the detection performance of infrared and visible light target detection methods in complex environments such as uneven lighting,overlapping objects,and diverse object scales,a multimodal object detection model based on convolution and self-attention mechanisms is designed by combining convolutional neural networks with visual attention networks.The model adopts a lightweight network design concept to construct a multi-branch feature extraction structure based on the characteristics of infrared and visible light images.The visible light branch uses group convolution units as the core to progressively extract image texture details feature from a local perspective and the infrared branch utilizes an efficient multi-head self-attention module based on windows to extract significant image features from a global perspective.For different modal features,a cross-modal information fusion structure is designed to achieve complementarity between infrared and visible light features through key feature enhancement and dynamic adjustment of fusion weights.Meanwhile,to enhance the scale invariance of the model,a multi-scale feature filtering fusion structure is introduced to alleviate information conflicts between features of different dimensions from both channel and spatial location perspectives.Experiments on standard datasets show that compared with current similar methods,this method achives the detection accuracy of 85.3%in complex scenes,and it also has higher generalization ability and robustness.关键词
可见光-红外目标检测/卷积网络/视觉注意力网络/特征融合Key words
visible-infrared object detection/convolutional network/visual attention network/feature fusion分类
信息技术与安全科学引用本文复制引用
周聪敏,吴斌鹏..融合卷积与自注意力机制的可见光-红外目标检测[J].电讯技术,2026,66(8):1308-1316,9.基金项目
国家自然科学基金资助项目(51977074) （51977074）
河南省社科联调研课题(SKL2019888) （SKL2019888）
