PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection
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 in...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/12/2072 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mi>o</mi><mi>U</mi></mrow></semantics></math></inline-formula> of 80.8%, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mi>d</mi></msub></semantics></math></inline-formula> of 95.1%, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mi>a</mi></msub></semantics></math></inline-formula> of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>28.6</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>6</mn></mrow></msup></mrow></semantics></math></inline-formula> on the BIT-SIRST dataset. |
|---|---|
| ISSN: | 2072-4292 |