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Recent Advances in Infrared Target Detection
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Recent Advances in Infrared Target Detection

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5285

Special Issue Editors

Dr. Xia Wang

E-Mail Website
Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
Interests: infrared imaging; infrared target detection; imaging processing
Prof. Dr. Kun Gao

E-Mail Website
Guest Editor
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
Interests: infrared imaging; infrared target detection; imaging processing

Special Issue Information

Dear Colleagues,

Infrared target detection technology offers advantages due to its strong anti-interference capability, high concealment, and all-weather functionality. This technology has been widely applied in ecosystem services, urban monitoring, and environmental protection.

Significant progress has been made in both infrared detection technology and detection algorithms in recent years. Deep learning-based target detection systems have gained considerable attention, while new large-area-array infrared detectors have enhanced the application of detection algorithms in the mainstream field, resulting in notable application features, including vast amounts of image data, the introduction of edge computing, and the integration of multi-sensor and multi-modal data.

Currently, challenges remain regarding the accuracy, real-time performance, and robustness of infrared target detection methods. Therefore, we look forward to researchers in this area proposing innovative approaches.

This Special Issue aims to collect submissions in infrared target object detection, identification, and tracking, including theoretical, methodological papers, and technical application studies, to present the latest advancements and research findings in multi-band, multi-dimensional, and multi-scale infrared target detection.

  • Detection and identification of infrared target objects for urban and infrastructure monitoring;
  • Detection and identification of infrared target objects for agricultural monitoring;
  • Detection, tracking, and identification of infrared target objects for security applications;
  • Detection, tracking, and identification of infrared target objects for environmental monitoring;
  • Infrared datasets for target object detection and identification;
  • Calibration technology for infrared remote sensors;
  • Infrared multispectral/hyperspectral target detection;
  • Multi-sensor (spatiotemporal and multimodal) data fusion for target object detection and identification;
  • Methods, algorithms, and theoretical models for target object detection, tracking, and identification.

Dr. Xia Wang
Prof. Dr. Kun Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • infrared target detection
  • deep learning
  • signal processing
  • infrared image enhancement
  • multi-resource data fusion
  • infrared target tracking
  • infrared target monitoring

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Published Papers (7 papers)

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Research

31 pages, 7540 KiB  
Article
Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition
by Zhihao Liu, Weiqi Jin and Li Li
Remote Sens. 2025, 17(8), 1343; https://doi.org/10.3390/rs17081343 - 9 Apr 2025
Viewed by 369
Abstract
Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. [...] Read more.
Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. Additionally, the Laplacian operator-based bidirectional twisted tensor truncated nuclear norm (bt-LPTNN), is proposed, which is a norm that automatically assigns weights to different singular values based on their importance. Furthermore, a weighted spatiotemporal total variation regularization method for nonconvex tensor approximation is employed to preserve scene details. To recover spatial domain information lost during tensor estimation, robust principal component analysis is employed, and spatial information is extracted from the noise tensor. The proposed model, bt-LPTVTD, is solved using an augmented Lagrange multiplier algorithm, which outperforms several state-of-the-art algorithms. Compared to some of the latest algorithms, bt-LPTVTD demonstrates improvements across all evaluation metrics. Extensive experiments conducted using complex scenes underscore the strong adaptability and robustness of our algorithm. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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28 pages, 14703 KiB  
Article
FTIR-SpectralGAN: A Spectral Data Augmentation Generative Adversarial Network for Aero-Engine Hot Jet FTIR Spectral Classification
by Shuhan Du, Yurong Liao, Rui Feng, Fengkun Luo and Zhaoming Li
Remote Sens. 2025, 17(6), 1042; https://doi.org/10.3390/rs17061042 - 16 Mar 2025
Viewed by 520
Abstract
Aiming at the overfitting problem caused by the limited sample size in the spectral classification of aero-engine hot jets, this paper proposed a synthetic spectral enhancement classification network FTIR-SpectralGAN for the FT-IR of aeroengine hot jets. Firstly, passive telemetry FTIR spectrometers were used [...] Read more.
Aiming at the overfitting problem caused by the limited sample size in the spectral classification of aero-engine hot jets, this paper proposed a synthetic spectral enhancement classification network FTIR-SpectralGAN for the FT-IR of aeroengine hot jets. Firstly, passive telemetry FTIR spectrometers were used to measure the hot jet spectrum data of six types of aero-engines, and a spectral classification dataset was created. Then, a spectral classification network FTIR-SpectralGAN was designed, which consists of a generator and a discriminator. The generator architecture comprises six Conv1DTranspose layers, with five of these layers integrated with BN and LeakyReLU layers to introduce noise injection. This design enhances the generation capability for complex patterns and facilitates the transformation from noise to high-dimensional data. The discriminator employs a multi-task dual-output structure, consisting of three Conv1D layers combined with LeakyReLU and Dropout techniques. This configuration progressively reduces feature dimensions and mitigates overfitting. During training, the generator learns the underlying distribution of spectral data, while the discriminator distinguishes between real and synthetic data and performs spectral classification. The dataset was randomly partitioned into training, validation, and test sets in an 8:1:1 ratio. For training strategy, an unbalanced alternating training approach was adopted, where the generator is trained first, followed by the discriminator and then the generator again. Additionally, weighted mixed loss and label smoothing strategies were introduced to enhance network training performance. Experimental results demonstrate that the spectral classification accuracy reaches up to 99%, effectively addressing the overfitting issue commonly encountered in CNN-based classification tasks with limited samples. Comparative experiments show that FTIR-SpectralGAN outperforms classical data augmentation methods and CVAE-based synthetic data enhancement approaches. It also achieves higher robustness and classification accuracy compared to other spectral classification methods. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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22 pages, 9566 KiB  
Article
Infrared Imaging Detection for Hazardous Gas Leakage Using Background Information and Improved YOLO Networks
by Minghe Wang, Dian Sheng, Pan Yuan, Weiqi Jin and Li Li
Remote Sens. 2025, 17(6), 1030; https://doi.org/10.3390/rs17061030 - 15 Mar 2025
Viewed by 706
Abstract
Hazardous gas leakage in the petrochemical industry frequently results in major incidents. A significant challenge arises due to the limitations of the current gas plume target feature extraction and identification techniques, which reduce the automated detection capabilities of remote monitoring systems. To address [...] Read more.
Hazardous gas leakage in the petrochemical industry frequently results in major incidents. A significant challenge arises due to the limitations of the current gas plume target feature extraction and identification techniques, which reduce the automated detection capabilities of remote monitoring systems. To address this, we propose BBGFA-YOLO, a real-time detection method leveraging background information and an improved YOLO network. This approach is designed specifically for the infrared imaging of gas plume targets, fulfilling the requirements of visual remote monitoring for hazardous gas leaks. We introduce a synthetic image colorization method based on background estimation, which leverages background estimation techniques to integrate motion features from gas plumes within the synthesized images. The resulting dataset can be directly employed by existing target detection networks. Furthermore, we introduce the MSDC-AEM, an attention enhancement module based on multi-scale deformable convolution, designed to enhance the network’s perception of gas plume features. Additionally, we incorporate an improved C2f-WTConv module, utilizing wavelet convolution, within the neck stage of the YOLO network. This modification strengthens the network’s capacity to learn deep gas plume features. Finally, to further optimize the network performance, we pre-train the network using a large-scale smoke detection dataset that includes reference background information. The experimental results, based on our self-acquired gas plume dataset, demonstrate a significant improvement in detection accuracy with the BBGFA-YOLO method, specifically achieving an increase in the average precision (AP50) from 74.2% to 96.2%. This research makes a substantial contribution to industrial hazardous gas leak detection technology, automated alarm systems, and the development of advanced monitoring equipment. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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20 pages, 7366 KiB  
Article
Histogram of Polarization Gradient for Target Tracking in Infrared DoFP Polarization Thermal Imaging
by Jianguo Yang, Dian Sheng, Weiqi Jin and Li Li
Remote Sens. 2025, 17(5), 907; https://doi.org/10.3390/rs17050907 - 4 Mar 2025
Viewed by 504
Abstract
Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram [...] Read more.
Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram of polarization gradient (HPG) feature descriptor that enables efficient feature representation of polarization mosaic images. First, a polarization distance calculation model based on normalized cross-correlation (NCC) and local variance is constructed, which enhances the robustness of gradient feature extraction through dynamic weight adjustment. Second, a sparse Laplacian filter is introduced to achieve refined gradient feature representation. Subsequently, adaptive polarization channel correlation weights and the second-order gradient are utilized to reconstruct the degree of linear polarization (DoLP). Finally, the gradient and DoLP sign information are ingeniously integrated to enhance the capability of directional expression, thus providing a new theoretical perspective for polarization mosaic image structure analysis. The experimental results obtained using a self-developed long-wave infrared DoFP polarization thermal imaging system demonstrate that, within the same FBACF tracking framework, the proposed HPG feature descriptor significantly outperforms traditional grayscale {8.22%, 2.93%}, histogram of oriented gradient (HOG) {5.86%, 2.41%}, and mosaic gradient histogram (MGH) {27.19%, 18.11%} feature descriptors in terms of precision and success rate. The processing speed of approximately 20 fps meets the requirements for real-time tracking applications, providing a novel technical solution for polarization imaging applications. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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20 pages, 22620 KiB  
Article
Adaptive Differential Event Detection for Space-Based Infrared Aerial Targets
by Lan Guo, Peng Rao, Cong Gao, Yueqi Su, Fenghong Li and Xin Chen
Remote Sens. 2025, 17(5), 845; https://doi.org/10.3390/rs17050845 - 27 Feb 2025
Viewed by 572
Abstract
Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the [...] Read more.
Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an mAP@0.5 of 96.3%, an FR of 4.3 ×10−5, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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22 pages, 52708 KiB  
Article
CSMR: A Multi-Modal Registered Dataset for Complex Scenarios
by Chenrui Li, Kun Gao, Zibo Hu, Zhijia Yang, Mingfeng Cai, Haobo Cheng and Zhenyu Zhu
Remote Sens. 2025, 17(5), 844; https://doi.org/10.3390/rs17050844 - 27 Feb 2025
Viewed by 603
Abstract
Complex scenarios pose challenges to tasks in computer vision, including image fusion, object detection, and image-to-image translation. On the one hand, complex scenarios involve fluctuating weather or lighting conditions, where even images of the same scenarios appear to be different. On the other [...] Read more.
Complex scenarios pose challenges to tasks in computer vision, including image fusion, object detection, and image-to-image translation. On the one hand, complex scenarios involve fluctuating weather or lighting conditions, where even images of the same scenarios appear to be different. On the other hand, the large amount of textural detail in the given images introduces considerable interference that can conceal the useful information contained in them. An effective solution to these problems is to use the complementary details present in multi-modal images, such as visible-light and infrared images. Visible-light images contain rich textural information while infrared images contain information about the temperature. In this study, we propose a multi-modal registered dataset for complex scenarios under various environmental conditions, targeting security surveillance and the monitoring of low-slow-small targets. Our dataset contains 30,819 images, where the targets are labeled as three classes of “person”, “car”, and “drone” using Yolo format bounding boxes. We compared our dataset with those used in the literature for computer vision-related tasks, including image fusion, object detection, and image-to-image translation. The results showed that introducing complementary information through image fusion can compensate for missing details in the original images, and we also revealed the limitations of visual tasks in single-modal images with complex scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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21 pages, 9019 KiB  
Article
Aberration Modulation Correlation Method for Dim and Small Space Target Detection
by Changchun Jiang, Junwei Li, Shengjie Liu and Hao Xian
Remote Sens. 2024, 16(19), 3729; https://doi.org/10.3390/rs16193729 - 8 Oct 2024
Viewed by 977
Abstract
The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration [...] Read more.
The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems. Full article
(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)
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