Context modulation asymmetric fusion U-net for infrared small target detection

U-Net has gained widespread application in image segmentation due to its symmetric structure design and skip connection mechanism. However, in the detection of small infrared targets, issues such as complex environmental backgrounds, small size of targets, and inherent characteristics (low signal-to-noise ratio, background clutter interference, low contrast between target and background) make it difficult for U-Net to effectively reconstruct target shapes during the decoding stage; this also exacerbates feature confusion, leading to an inability to achieve differentiated feature fusion. To address this issue, this paper proposes a non-symmetric context fusion attention U-Net. This method focuses on target responses and context during the encoding stage, extracting target features and contextual information at both shallow and deep levels. In the decoding stage, these extracted features are fused and gradually refined to enhance target response, yielding more accurate results. Experimental results show that CMAF demonstrates excellent performance on two public datasets.