MSCA-Net:Multi-Scale Context Aggregation Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2503.17193v1
• Date: Fri, 21 Mar 2025 14:42:31 GMT
• Title: MSCA-Net:Multi-Scale Context Aggregation Network for Infrared Small Target Detection
• Authors: Xiaojin Lu, Taoran yue, Jiaxi cai, Shibing Chu,
• Abstract summary: This paper proposes a novel network architecture named MSCA-Net, which integrates three key components.<n>MSEDA employs a multi-scale feature fusion attention mechanism to adaptively aggregate information across different scales.<n>PCBAM captures the correlation between global and local features through a correlation matrix-based strategy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting infrared small targets in complex backgrounds remains a challenging task 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 novel network architecture named MSCA-Net, which integrates three key components: Multi-Scale Enhanced Detection Attention mechanism(MSEDA), Positional Convolutional Block Attention Module (PCBAM), and Channel Aggregation 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 redistributes input feature channels, facilitating the efficient transmission of beneficial features and further enhancing the model detection capability in complex backgrounds. The experimental results demonstrate that MSCA-Net achieves outstanding 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.

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