A Pixel Expansion-Based Improvement in Dense Nesting Structures for Infrared Small Target Detection

In infrared detection, the identification of weak targets is often hindered by low pixel count and resolution, leading to a scarcity of semantic details for small targets and significant blurring of boundary information. To solve these problems, we propose a novel approach for the enhanced cross-sta...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5
Hlavní autoři: Zhao, Zhichao, Wang, Hao, Li, Haiyan, Yang, Jundon, Yu, Pengfei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Ambient noise
Attention mechanisms
Background noise
Convolution
Convolutional block attention module
convolutional neural networks (CNNs)
Decoding
densely nested interactive modules
Encoding
Feature extraction
Image edge detection
Image processing
Information transfer
infrared small target detection (IRSTD)
Matching
Medical imaging
Modules
Monte Carlo methods
Nesting
Object detection
Pixels
Semantics
Target detection
Training
ISSN:1545-598X, 1558-0571
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Shrnutí:In infrared detection, the identification of weak targets is often hindered by low pixel count and resolution, leading to a scarcity of semantic details for small targets and significant blurring of boundary information. To solve these problems, we propose a novel approach for the enhanced cross-stage feature matching network (ECFNet) to learn features beyond a single-scale data source by introducing pixel expansion branching in this letter. First, we introduce the feature attention enhancement module (FAEM), which uses rapid edge feature extraction to effectively enhance the boundary information of weak targets after pixel expansion, thereby improving the network's fine-grained detection capability. Moreover, inspired by the Monte Carlo attention mechanism used in medical image processing, we introduce the stage randomness enhancement module (SREM) to direct the network's focus toward small target regions rather than background noise during the learning process, allowing the network to adapt to various random situations independent of a fixed structure. Furthermore, we design a cross-feature matching module (CFMM), which effectively aggregates shallow profile information and deeper semantics at the center of the network, facilitating efficient information transfer and precise feature assignment, thereby narrowing the feature gap between the encoding and decoding stages. Our network achieves an intersection-over-union (IoU) ratio of 78.51% on the publicly available NUAA-SIRST dataset. The experimental results of NUDT-SIRDT and infrared small target detection (IRSTD)-1k can be found in https://github.com/bobo66597/ECF .
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2025.3547899