📝 SummarXiv

Abstract

In complex environments, detecting tiny infrared targets has always been challenging because of the low contrast and high noise levels inherent in infrared images. These factors often lead to the loss of crucial details during feature extraction. Moreover, existing detection methods have limitations in adequately integrating global and local information, which constrains the efficiency and accuracy of infrared small target detection. To address these challenges, this paper proposes a network architecture named MSCA-Net, which integrates three key components: Multi-Scale Enhanced Dilated Attention mechanism (MSEDA), Positional Convolutional Block Attention Module (PCBAM), and Channel Aggregation Feature Fusion Block (CAB). Specifically, MSEDA employs a multi-scale feature fusion attention mechanism to adaptively aggregate information across different scales, enriching feature representation. PCBAM captures the correlation between global and local features through a correlation matrix-based strategy, enabling deep feature interaction. Moreover, CAB enhances the representation of critical features by assigning greater weights to them, integrating both low-level and high-level information, and thereby improving the models detection performance in complex backgrounds. The experimental results demonstrate that MSCA-Net achieves strong small target detection performance in complex backgrounds. Specifically, it attains mIoU scores of 78.43%, 94.56%, and 67.08% on the NUAA-SIRST, NUDT-SIRST, and IRTSD-1K datasets, respectively, underscoring its effectiveness and strong potential for real-world applications.