IR-ADMDet: An Anisotropic Dynamic-Aware Multi-Scale Network for Infrared Small Target Detection
Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. To address these issues, we propose an Anisotropic Dynamic-aware Multi-scale Network for Infrared Small Target Detection...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/10/1694 |
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| Summary: | Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. To address these issues, we propose an Anisotropic Dynamic-aware Multi-scale Network for Infrared Small Target Detection (IR-ADMDet). The core of IR-ADMDet is a Dual-Path Hybrid Feature Extractor Network (DPHFENet). This network effectively synergizes local residual learning with global context modeling. It enhances faint target signatures while suppressing interference. Additionally, a Hierarchical Adaptive Fusion Framework (HAFF) is utilized. HAFF integrates bidirectional gating, recursive graph enhancement, and interlink fusion. This framework optimally refines features across multiple scales. The entire architecture is optimized for efficiency using dynamic feature recalibration. Extensive experiments were conducted on benchmark datasets including SIRSTv2, IRSTD-1k, and NUDT-SIRST. These experiments demonstrate the superiority of IR-ADMDet. It achieves state-of-the-art (SOTA) results, such as 0.96 AP50 and 0.95 F1-score on SIRSTv2. This performance is achieved with significantly fewer parameters, only 5.77 M, compared to existing methods. This shows remarkable robustness in low-contrast, high-noise scenarios. IR-ADMDet also outperforms contemporary segmentation-based approaches. |
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| ISSN: | 2072-4292 |