WT-HMFF: Wavelet Transform Convolution and Hierarchical Multi-Scale Feature Fusion Network for Detecting Infrared Small Targets
Infrared small target detection (ISTD) means distinguishing small and faint targets from IR images. Small targets typically span only a handful of pixels, lacking distinct texture and clear structural details. For the past few years, deep learning has made big strides in the field of ISTD. Yet, a pe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2268 |
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| Summary: | Infrared small target detection (ISTD) means distinguishing small and faint targets from IR images. Small targets typically span only a handful of pixels, lacking distinct texture and clear structural details. For the past few years, deep learning has made big strides in the field of ISTD. Yet, a persistent challenge remains: the lack of high-level semantic information may cause the disappearance of small target features in the network’s deep layers, ultimately impairing detection accuracy. To tackle this problem, we introduce WT-HMFF, an innovative network architecture that combines the Wavelet Transform Convolution (WTConv) module with the Hierarchical Multi-Scale Feature Fusion (HMFF) module to enhance the ISTD algorithm’s performance. WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. The HMFF module enables the efficient fusion of shallow and deep features, maintaining the high resolution of deep feature maps and preventing the disappearance of small target features. We have tested it out on public datasets, SIRST and IRSTD-1k, and validated the superiority and robustness of WT-HMFF compared to other methods. |
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| ISSN: | 2072-4292 |