Infrared small-target detection via tensor construction and decomposition

Chen, Zhenguo; Chen, Shuizhong; Zhai, Zhengjun; Zhao, Mingjing; Jie, Feiran; Li, Wei

doi:10.1080/2150704x.2021.1944689

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Nevertheless, these methods have limitations of concurrently incorporating both spatial-and frequency-domain information, which restricts the effectiveness of their practical application. With the application of wavelet transform and local contrast enhancement [16,23,24], it becomes feasible to effectively extract spatial-and frequency-domain information from infrared images. In addition, the recent development of deep learning techniques has brought new ideas for dual-domain feature extraction from infrared images.…”

Section: High-frequency Characteristics Of Infrared Small Targetsmentioning

confidence: 99%

“…Our proposed method focuses on existing domain knowledge to guide the model at both the input and the inner levels of the network. Given that the development of recovering the low-rank and sparse matrices is well established, the infrared patch-image model [15][16][17] based on unsupervised learning offers a reliable theoretical basis for the sparse-characteristic-driven module. In addition, targets in infrared imagery exhibit higher radiation intensity than the background, which causes distinct high-frequency characteristics in the frequency domain.…”

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confidence: 99%

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Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection

Hao,

Liu,

Zhao

et al. 2023

Remote Sensing

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In recent years, data-driven deep networks have demonstrated remarkable detection performance for infrared small targets. However, continuously increasing the depth of neural networks to enhance performance has proven impractical. Consequently, the integration of prior physical knowledge related to infrared small targets within deep neural networks has become crucial. It aims to improve the models’ awareness of inherent physical characteristics. In this paper, we propose a novel dual-domain prior-driven deep network (DPDNet) for infrared small-target detection. Our method integrates the advantages of both data-driven and model-driven methods by leveraging the prior physical characteristics as the driving force. Initially, we utilize the sparse characteristics of infrared small targets to boost their saliency at the input level of the network. Subsequently, a high-frequency feature extraction module, seamlessly integrated into the network’s backbone, is employed to excavate feature information. DPDNet simultaneously emphasizes the prior sparse characteristics of infrared small targets in the spatial domain and their prior high-frequency characteristics in the frequency domain. Compared with previous CNN-based methods, our method achieves superior performance while utilizing fewer convolutional layers. It has a performance of 78.64% IoU, 95.56 Pd, and 2.15 × 10−6 Fa on the SIRST dataset.

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Section: High-frequency Characteristics Of Infrared Small Targetsmentioning

confidence: 99%

mentioning

confidence: 99%

Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection

Hao,

Liu,

Zhao

et al. 2023

Remote Sensing

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“…As a kind of artificial neural network, a deep neural network can independently construct (train) basic rules according to sample data in the Learning process. It has achieved excellent performance in more and more application fields, especially in the field of target detection [1],image recognition [2],software engineering [3],and so on [4].Neural networks are trained by supervised learning, that is, by sample data and predefined results of sample data. In simple terms, deep learning is the imitation of neurons in the form of a layer to get the data characteristics and store features in an artificial neural network, and the difference is that the network is a black box [5], just like the human brain.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the performance of the neural network by using a mini dataset processing method

chen

2022

Preprint

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Data processing is one of the essential methods to optimize the performance of neural networks. In this paper, we give up the traditional data processing method and propose a method to optimize the deep neural network by processing the mini data set based on the loss. Using this method, each Literation training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch finally At the same time, in order to verify the effectiveness of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyperparameters, and finally, the effectiveness of this data processing method is verified. On this basis, the advantages and disadvantages of this method are summarized. Finally, the future research direction has been prospected.

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A double-layer feature fusion convolutional neural network for infrared small target detection

Dandan

Pang

et al. 2023

International Journal of Remote Sensing

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Infrared small-target detection via tensor construction and decomposition

Cited by 5 publications

References 17 publications

Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection

Dual-Domain Prior-Driven Deep Network for Infrared Small-Target Detection

Optimizing the performance of the neural network by using a mini dataset processing method

A double-layer feature fusion convolutional neural network for infrared small target detection

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