MCC-Net: Efficient Dual-Attention Network for Infrared Small-Target Detection.
Saved in:
| Title: | MCC-Net: Efficient Dual-Attention Network for Infrared Small-Target Detection. |
|---|---|
| Authors: | Zhou, Xiaotian1,2 (AUTHOR), Wang, Xin1,2 (AUTHOR), Tian, Yan1,2 (AUTHOR) tianyan@opt.ac.cn, Jiang, Kai1,2 (AUTHOR), Guo, Min1,2 (AUTHOR), Lian, Xuezheng1,2 (AUTHOR), Ding, Lu1,2 (AUTHOR), Zhang, Quanyu1,2 (AUTHOR), Xue, Yaqi1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1858. 20p. |
| Subjects: | Computational complexity, Signal detection, Convolutional neural networks, Artificial neural networks, Deep learning, Image segmentation |
| Abstract: | Highlights: What are the main findings? We propose MCC-Net, an innovative infrared small-target detection method featuring a streamlined architecture with a complementary spatial-channel dual-attention mechanism. The proposed model integrates three innovative strategies, namely, Magnitude-Aware Linear Attention, Conditionally Parameterized Convolutions, and Conditional Cross-Channel Fusion, achieving superior detection performance while substantially reducing computational overhead. What are the implications of the main findings? The proposed method achieves state-of-the-art performance across multiple evaluation metrics and visual results on three public benchmark datasets. MCC-Net demonstrates substantially lower computational complexity than state-of-the-art methods, enabling efficient deployment in resource-constrained scenarios. Recent years have witnessed the emergence of numerous U-shaped deep learning segmentation methods for infrared small-target detection (IRSTD). However, increasingly complex models still suffer from false and missed detections in challenging scenarios with cluttered backgrounds and weak targets while incurring escalating computational costs. To address these limitations, this paper proposes MCC-Net, a novel and efficient IRSTD framework that achieves superior detection performance with significantly reduced computational complexity. First, we integrate Magnitude-Aware Linear Attention (MALA) and Conditionally Parameterized Convolutions (CondConv) to replace conventional attention mechanisms in skip connections and standard convolutions, respectively, endowing the model with spatial contextual modeling and enhanced feature extraction capabilities at minimal computational overhead. Second, we design an innovative Conditional Cross-Channel Fusion (CondCCF) module that establishes a complementary spatial-channel dual-attention mechanism with MALA, enabling efficient multi-scale feature fusion. Extensive comparative and ablation experiments conducted on three public benchmarks—SIRST-v1, NUDT-SIRST, and IRSTD-1K—demonstrate that MCC-Net achieves state-of-the-art mIoU scores of 77.98%, 95.43%, and 70.46%, respectively, surpassing state-of-the-art methods by 1.07%, 1.95%, and 0.95%. MCC-Net also outperforms existing approaches across multiple evaluation metrics while maintaining substantially lower computational complexity. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? We propose MCC-Net, an innovative infrared small-target detection method featuring a streamlined architecture with a complementary spatial-channel dual-attention mechanism. The proposed model integrates three innovative strategies, namely, Magnitude-Aware Linear Attention, Conditionally Parameterized Convolutions, and Conditional Cross-Channel Fusion, achieving superior detection performance while substantially reducing computational overhead. What are the implications of the main findings? The proposed method achieves state-of-the-art performance across multiple evaluation metrics and visual results on three public benchmark datasets. MCC-Net demonstrates substantially lower computational complexity than state-of-the-art methods, enabling efficient deployment in resource-constrained scenarios. Recent years have witnessed the emergence of numerous U-shaped deep learning segmentation methods for infrared small-target detection (IRSTD). However, increasingly complex models still suffer from false and missed detections in challenging scenarios with cluttered backgrounds and weak targets while incurring escalating computational costs. To address these limitations, this paper proposes MCC-Net, a novel and efficient IRSTD framework that achieves superior detection performance with significantly reduced computational complexity. First, we integrate Magnitude-Aware Linear Attention (MALA) and Conditionally Parameterized Convolutions (CondConv) to replace conventional attention mechanisms in skip connections and standard convolutions, respectively, endowing the model with spatial contextual modeling and enhanced feature extraction capabilities at minimal computational overhead. Second, we design an innovative Conditional Cross-Channel Fusion (CondCCF) module that establishes a complementary spatial-channel dual-attention mechanism with MALA, enabling efficient multi-scale feature fusion. Extensive comparative and ablation experiments conducted on three public benchmarks—SIRST-v1, NUDT-SIRST, and IRSTD-1K—demonstrate that MCC-Net achieves state-of-the-art mIoU scores of 77.98%, 95.43%, and 70.46%, respectively, surpassing state-of-the-art methods by 1.07%, 1.95%, and 0.95%. MCC-Net also outperforms existing approaches across multiple evaluation metrics while maintaining substantially lower computational complexity. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111858 |
