Dual-Stream Edge-Target Learning Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is crucial in both military and civilian applications. However, challenges such as low contrast, low signal-to-noise ratio (SNR), and lack of shape and texture information limit the effectiveness of existing methods in capturing edge details and representing target areas. To address these issues, we propose the dual-stream edge-target learning network (DETL-Net) for IRSTD. This network enhances feature cross-fusion by learning edge details and target regions through a dual-stream framework, significantly improving detection performance. Specifically, we extract multilevel features of the image based on the encoder-decoder structure of U-Net and then reconstruct the feature map. In the decoder, we propose the dual-guided cross-fusion module (DGCFM) to capture edge details of small targets and global contextual features of the target region, achieving complementary advantages. The multiscale context fusion module (MCFM) within DGCFM uses central difference convolution to enhance local contrast and extract rich contextual details, thereby retaining edge information and enhancing overall target representation. In addition, we introduce the cross-dimension interactive aggregation attention module (CIAAM), which dynamically adjusts feature fusion weights across layers to effectively suppress noise and enhance the discrimination of small targets. These modules are sequentially interconnected to progressively refine edge details, and the acquired target features are subsequently utilized for predicting the final target mask via the segmentation head. Experiments on the NUAA-SIRST and IRSTD-1k datasets demonstrate that DETL-Net outperforms state-of-the-art (SOTA) methods. The source code is available at https://github.com/rayyao/DETL-Net.