基于可见光与红外图像融合的行人检测方法OA

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.