基于红外双波段交叉注意力融合的空中目标抗干扰识别算法OA

This paper studies an anti-interference recognition algorithm based on infrared dual-band feature cross-fusion to address the problem of infrared aerial targets being disturbed by infrared de-coys.First,a dual-branch backbone network is designed to extract features from medium-wave in-frared(MWIR)and long-wave infrared(LWIR)images separately.To reduce redundant informa-tion in the convolutional layer output of single-band images,a segmentation-transformation-fusion feature extraction strategy is proposed.This strategy improves feature representation,reduces channel redundancy,and is reused multiple times in the backbone network to enhance efficiency and compactness.Second,a cross-fusion module is constructed to explore complementary informa-tion between the two infrared bands.This module models the long-range dependency of cross-band features and improves resistance to infrared decoy interference.It effectively captures the comple-mentary relationship between MWIR and LWIR features,enhancing target recognition stability.Finally,the experimental results on a simulated infrared dual-band image dataset show that the proposed algorithm achieves an average anti-interference recognition accuracy of 81.8％,which is 3.3％higher than YOLOv7.