WTAPNet: Wavelet Transform-Based Augmented Perception Network for Infrared Small-Target Detection

Hongying He

doi:10.1109/TIM.2024.3476549

WTAPNet: Wavelet Transform-Based Augmented Perception Network for Infrared Small-Target Detection

2024-01-01

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DOI：10.1109/TIM.2024.3476549

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Infrared (IR) small-target detection plays a vital role in various applications of both civil and military areas, such as marine rescue, forest fire prevention, and precise guidance. However, challenges stemming from the small size of IR targets and noise interference in complex backgrounds often limit the accuracy of IR small-target detection algorithms. Owing to the rapid development of deep learning, numerous convolutional neural networks (CNNs)-based methods have emerged in recent years, but they are inevitable to encounter the risk of target loss in deep layers due to the use of pooling layers. To address this problem, we present a wavelet transform-based augmented perception network, namely WTAPNet, in this article. First, an enhancement and enlarge (EE) module is designed to improve the network's perceptual capability for IR small targets by magnifying image resolution and augmenting target features at the same time. Then, a discrete wavelet transform-based downsampling (DWTD) module and an inverse wavelet transform-based fusion (IWTF) module are proposed. These two modules collaboratively work to extract and fuse multiscale features, which simultaneously reduces information loss. Finally, a bottom-up path fusion strategy is exploited to highlight and preserve small-target features, which associate the lowest level with the highest level features and interconnect predictions from different hierarchical levels. Experimental results on the NUDT-SIRST dataset and the SIRST dataset demonstrate the superiority of our WTAPNet over other state-of-the-art IR small-target detection methods in terms of F1-measure, recall, and other indicators. Our codes are publicly available at https://github.com/MinjieWan/WTAPNet.

Keywords:

Object detection

Feature extraction

Discrete wavelet transforms

Clutter

Signal to noise ratio

Robustness

Image resolution

Computational modeling

Accuracy

Tensors

Discrete wavelet transform

infrared (IR) small-target detection

multiscale feature

network perception

path fusion

WTAPNet: Wavelet Transform-Based Augmented Perception Network for Infrared Small-Target Detection

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