Infrared small target segmentation networks: A survey

Kou, Renke; Wang, Chunping; Peng, Zhenming; Zhao, Zhihe; Chen, Yaohong; Han, Jinhui; Huang, Fuyu; Yu, Ying; Fu, Qiang

doi:10.1016/j.patcog.2023.109788

Cited by 60 publications

(9 citation statements)

References 37 publications

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“…Current research on deep learning-based algorithms typically focuses on feature fusion, local information, feature pyramids, contextual information, etc. [33]. However, when dealing with complex sea surface backgrounds, the issue of high false alarm rates persists.…”

Section: Infrared Small Target Detection Networkmentioning

confidence: 99%

Multi-Attention Pyramid Context Network for Infrared Small Ship Detection

Guo,

Ma,

et al. 2024

JMSE

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In the realm of maritime target detection, infrared imaging technology has become the predominant modality. Detecting infrared small ships on the sea surface is crucial for national defense and maritime security. However, the challenge of detecting infrared small targets persists, especially in the complex scenes of the sea surface. As a response to this challenge, we propose MAPC-Net, an enhanced algorithm based on an existing network. Unlike conventional approaches, our method focuses on addressing the intricacies of sea surface scenes and the sparse pixel occupancy of small ships. MAPC-Net incorporates a scale attention mechanism into the original network’s multi-scale feature pyramid, enabling the learning of more effective scale feature maps. Additionally, a channel attention mechanism is introduced during the upsampling process to capture relationships between different channels, resulting in superior feature representations. Notably, our proposed Maritime-SIRST dataset, meticulously annotated for infrared small ship detection, is introduced to stimulate advancements in this research domain. Experimental evaluations on the Maritime-SIRST dataset demonstrate the superiority of our algorithm over existing methods. Compared to the original network, our approach achieves a 6.14% increase in mIOU and a 4.41% increase in F1, while maintaining nearly unchanged runtime.

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Section: Infrared Small Target Detection Networkmentioning

confidence: 99%

Multi-Attention Pyramid Context Network for Infrared Small Ship Detection

Guo,

Ma,

et al. 2024

JMSE

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“…S INGLE-FRAME infrared small target detection (SISTD) is a critical task that separates small and dim targets from complex backgrounds like the sky, ocean, and urban structures. It plays an essential role in various fields, encompassing defense security [1], [2], maritime surveillance [2]- [4], and precision guidance [2], [5]. Nevertheless, it poses particular challenges.…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Zhu,

Ma,

Fan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Extracting small targets from complex backgrounds is the eventual goal of single infrared small target detection (SISTD), which has many potential applications in defense security and marine rescue. Recently, methods utilizing deep learning have shown their superiority over traditional theoretical approaches. However, they do not consider both the global semantics and specific shape information, thereby limiting their performance. To overcome this proplem, we propose a Gated Shaped TransUnet (GSTUnet), designed to fully utilize shape information while detecting small target detection. Specifically, we have proposed a multi-scale encoder branch to extract global features of small targets at different scales. Then, the extracted global features are passed through a gated-shaped stream branch that focuses on the shape information of small targets through gate convolutions. Finally, we fuse there features to obtain the final result. Our GSTUnet learns both global and shape information through the aforementioned two branches, establishing global relationships between different feature scales. The GSTUnet demonstrates excellent evaluation metrics on various datasets, outperforming current state-of-the-art methods. To access our code and datasets, please visit the following link: https://github.com/

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“…Moreover, the solution process contains large-scale matrix operations and multiple iterative optimizations, leading to high complexity and poor real-time performance. With massive data and optimized models, NN-based methods have achieved impressive representation capabilities and detection performance [11]. However, they are not robust enough to detection scenarios that lack real data, limiting their wider application.…”

Section: Introductionmentioning

confidence: 99%

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

Chen,

Wu,

Zheng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared small target detection is critical to infrared search and tracking (IRST) systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the "target suppression" phenomenon tends to occur. To address this issue, we propose a robust unsupervised multi-feature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multi-targets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.

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Infrared small target segmentation networks: A survey

Cited by 60 publications

References 37 publications

Multi-Attention Pyramid Context Network for Infrared Small Ship Detection

Multi-Attention Pyramid Context Network for Infrared Small Ship Detection

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

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