Infrared Dual-band Target Detecting Fusion Algorithm Based on Multiple Features

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.