华中科技大学学报（自然科学版）2025，Vol.53Issue(3)：14-22,9.DOI:10.13245/j.hust.250210
基于双分支特征融合的跨模态行人检测算法
Cross-modal pedestrian detection algorithm based on dual-branch feature fusion
摘要
Abstract
The commonly used visible light single spectrum pedestrian detection is easily affected by the environment and lighting conditions.In night and complex environments,as well as when detecting occluded and small-scale targets,there may be large deviations or missed detections in the detection results.To address these issues,a cross modal pedestrian detection algorithm based on dual branch feature fusion was proposed.Within the YOLOv7 framework,the backbone network was enhanced into a dual-backbone structure to separately extract feature information from both visible light and infrared images.A differential cross fusion(DCF)module was designed to integrate these two modalities'features,followed by training the fused features for comprehensive pedestrian detection via the detection network.To enhance detection accuracy,an attention mechanism,SeNet module,was incorporated into the dual-backbone network.Furthermore,to boost efficiency,lightweight ELAN-G and ELAN-WT modules were designed to replace corresponding components in the original network architecture.Experimental results demonstrate that the detection accuracy of the proposed algorithm is consistently higher than that of existing cross-modal detection algorithms,while also satisfying real-time requirements.关键词
多模态深度学习/特征融合/行人检测/跨模态/YOLOv7框架Key words
multimodal deep learnin/feature fusion/pedestrian detection/cross-modal/YOLOv7 framework分类
计算机与自动化引用本文复制引用
陈广秋,张桐森,段锦,黄丹丹..基于双分支特征融合的跨模态行人检测算法[J].华中科技大学学报（自然科学版）,2025,53(3):14-22,9.基金项目
国家自然科学基金资助项目(62127813). （62127813）
