PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection
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| Publicado en: | Remote Sensing vol. 17, no. 12 (2025), p. 2072-2095 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | Given the widespread influence of U-Net and FPN network architectures on infrared small target detection tasks on existing models, these structures frequently incorporate a significant number of downsampling operations, thereby rendering the preservation of small target information and contextual interaction both challenging and computation-consuming. To tackle these challenges, we introduce a parallel connected lateral chain network (PCLC-Net), an innovative architecture in the domain of infrared small target detection, that preserves large-scale feature maps while minimizing downsampling operations. The PCLC-Net preserves large-scale feature maps to prevent small target information loss, integrates causal-based retention gates (CBR Gates) within each chain for improved feature selection and fusion, and leverages the attention-based network-wide feature map aggregation (AN-FMA) output module to ensure that all feature maps abundant with small target information contribute effectively to the model’s output. The experimental results reveal the PCLC-Net, with minimal nodes and just a single downsampling, achieves near state-of-the-art performance using just 0.16M parameters (40% of the current smallest model), yielding an <inline-formula>IoU</inline-formula> of 80.8%, <inline-formula>Pd</inline-formula> of 95.1%, and <inline-formula>Fa</inline-formula> of <inline-formula>28.6×10−6</inline-formula> on the BIT-SIRST dataset. |
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| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs17122072 |
| Fuente: | Advanced Technologies & Aerospace Database |