现代信息科技2026，Vol.10Issue(9)：75-79,5.DOI:10.19850/j.cnki.2096-4706.2026.09.014
基于可见光与红外图像融合的行人检测方法
Pedestrian Detection Method Based on Visible and Infrared Image Fusion
朱巨风1
作者信息
- 1. 广西民族大学 人工智能学院,广西 南宁 530006
- 折叠
摘要
Abstract
To address the issues of low pedestrian detection accuracy in low-light environments and susceptibility to false detections in complex environments,a multispectral object detection algorithm based on the Cross-Modal Feature Fusion Network(CFFNet)is proposed,utilizing the YOLO framework and fusing images from different spectral modalities(e.g.,RGB and thermal infrared images).Improving upon Transformer-based fusion methods,a Cross-Modal Feature Interaction Module(CFI)is designed,incorporating a cross-attention mechanism.This module mainly consists of two parts:a Spatial Feature Compression Module(SFS)and a Cross-Modal Information Complementary Module(CICM).SFS reduces computational cost while preserving important image features as much as possible.CICM focuses on efficiently mining and utilizing Complementary feature information between different modalities.Experimental results show that,compared to the Iterative Cross-Attention Guided Feature Fusion Algorithm(ICAFusion),CFFNet improves mAP@0.5 and mAP@0.5:0.95 by 3.8%and 1.3%respectively on the FLIR multispectral dataset,accurately detecting targets and reducing false detections.关键词
多光谱目标检测/Transformer/交叉注意力机制/YOLO/目标检测Key words
multispectral object detection/Transformer/cross-attention mechanism/YOLO/object detection分类
信息技术与安全科学引用本文复制引用
朱巨风..基于可见光与红外图像融合的行人检测方法[J].现代信息科技,2026,10(9):75-79,5.
