Small target detection in infrared image using convolutional neural networks

Wanting, Wang; Qin, Hanlin; Cheng, Wenxiong; Wang, Chunmei; Leng, Hanbing; Zhou, Huixin

doi:10.1117/12.2285689

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…Deep learning approaches [7]- [14] exhibit higher recognition accuracy than traditional methods without relying on specific scenes or devices, demonstrating increased robustness and significantly lower costs, gradually taking a dominant position in the field. Wang et al [15] used the model trained by ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data to complete the infrared small object detection task. Liangkui et al [16] combined with the data generated from oversampling, a multilayer network was proposed for small object detection.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

The Future Development of Education in the Era of Artificial Intelligence

Xu¹,

Li²,

Chen³

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared singleframe image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://github.com/zhengshuchen/HCFNet.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

The Future Development of Education in the Era of Artificial Intelligence

Xu¹,

Li²,

Chen³

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…In recent years, neural networks have emerged as a viable approach for detecting IR targets. Wang et al [18] utilized convolutional neural networks (CNNs) for extracting target features. Furthermore, the YOLO series, introduced by Redmon et al [19], and generative adversarial networks (GANs) proposed by Goodfellow et al [20] have been the subject of extensive research.…”

Section: Related Workmentioning

confidence: 99%

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Aliha,

Liu,

Zhou

et al. 2024

Remote Sensing

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Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

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“…Infrared patch-image (IPI) [25] In addition to the traditional detection methods mentioned above, deep learning has become increasingly popular in the detection of infrared small moving targets. Wang et al [32] applied a convolutional neural network (CNN) to extract IR target features, providing a reference for subsequent studies. Redmon et al [33] proposed the YOLO series, whereas Goodfellow et al [34] introduced generative adversarial networks (GAN), which have been widely studied, with various improved versions used for IR target detection.…”

Section: Authors Objective Contributionmentioning

confidence: 99%

A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes

Aliha,

Liu,

et al. 2023

Remote Sensing

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Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.

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Small target detection in infrared image using convolutional neural networks

Cited by 6 publications

References 15 publications

The Future Development of Education in the Era of Artificial Intelligence

The Future Development of Education in the Era of Artificial Intelligence

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes

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