基于多特征的红外双波段目标检测融合算法OA北大核心CSTPCD

Infrared target detection algorithms play important roles in the military and civilian fields and have been widely studied.However,relatively few studies have been conducted on the use of dual-band images for targeted detection.To fully utilize the advantages of dual-band images in target detection,a fusion algorithm based on multiple features of infrared dual-band images was proposed through an in-depth analysis of the detection results.The proposed fusion algorithm utilizes a deep learning-based multi-feature fusion network to process the detection results of dual-band images,fully mine the feature information of the target,adaptively select the detection results of a single band as the output,and obtain the final decision-level fusion detection results.The experimental results show that,compared with using single-band images for object detection,the proposed infrared dual-band fusion algorithm based on multiple features can effectively utilize information from different bands,improve the detection performance,and fully leverage the advantages of infrared object detection equipment.