{"title":"Multiscale Channel Attention and Cross-Layer Fusion Network for Infrared Small Target Detection","authors":"Shengli Zhou;Tong Liu;Xiaolu Guo;Meibo Lv","doi":"10.1109/LGRS.2025.3580709","DOIUrl":null,"url":null,"abstract":"Infrared small target detection (IRSTD) faces challenges due to limited global perception and feature ambiguity in complex scenarios. To address these issues, we propose a novel multiscale channel attention and cross-layer fusion network (MACFNet). The framework integrates three key innovations: 1)the feature convolution attention transformer (FCAT) addresses limited global perception by combining local features and global contexts to enhance target representation; 2) the efficient channel and spatial attention (ECSA) module resolves feature ambiguity by optimizing discriminative feature weighting; and 3) an enhanced M-UNet architecture incorporates channelwise cross fusion transformer (CCT) modules to enable effective cross-scale semantic alignment. Extensive experiments on the SIRST and NUDT-SIRST datasets demonstrate the state-of-the-art performance, achieving significantly higher IoU of 0.8396 and 0.9346, respectively, surpassing the existing model-driven and data-driven methods while maintaining a real-time capable inference speed of 29.7167 FPS.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11039771/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Infrared small target detection (IRSTD) faces challenges due to limited global perception and feature ambiguity in complex scenarios. To address these issues, we propose a novel multiscale channel attention and cross-layer fusion network (MACFNet). The framework integrates three key innovations: 1)the feature convolution attention transformer (FCAT) addresses limited global perception by combining local features and global contexts to enhance target representation; 2) the efficient channel and spatial attention (ECSA) module resolves feature ambiguity by optimizing discriminative feature weighting; and 3) an enhanced M-UNet architecture incorporates channelwise cross fusion transformer (CCT) modules to enable effective cross-scale semantic alignment. Extensive experiments on the SIRST and NUDT-SIRST datasets demonstrate the state-of-the-art performance, achieving significantly higher IoU of 0.8396 and 0.9346, respectively, surpassing the existing model-driven and data-driven methods while maintaining a real-time capable inference speed of 29.7167 FPS.