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Infrared Sensing and Target Detection
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Infrared Sensing and Target Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 17354

Special Issue Editors

Dr. Xiaoyang Wang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK
Interests: machine learning; signal processing; computer vision
Dr. Yuhan Liu

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: signal processing; remote sensing

Special Issue Information

Dear Colleagues,

Infrared sensing technology, target detection and tracking play an important role in remote sensing applications, search and tracking systems, security systems, etc. The imaging quality and lack of texture and color information have always been a challenge in infrared sensing and information processing. In recent years, infrared target detection approaches have been moving towards multimodality sensing and deep-learning-based processing, embracing the quick development of computer vision. This Special Issue encourages submissions concerning infrared sensing technologies, the latest infrared datasets, target detection and tracking algorithms, applications and systems. The proposed topics include (but are not limited to):

  • Infrared sensing and imaging technology;
  • Datasets of infrared images;
  • Infrared and visible image fusion;
  • Infrared image colorization;
  • Infrared image denoising;
  • Generative models for infrared sensing;
  • Self-supervised learning for infrared sensing;
  • Infrared super-resolution reconstruction;
  • Deep-learning-based infrared target detection;
  • Anomaly detection in infrared images;
  • Multiobject detection in infrared images;
  • Infrared small target detection;
  • Infrared target tracking;
  • Review and benchmarking for infrared sensing and target detection.

Dr. Xiaoyang Wang
Dr. Yuhan Liu
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. Sensors 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 2600 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 sensing
  • sensor fusion
  • infrared target detection
  • super-resolution
  • infrared target tracking

Published Papers (6 papers)

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Research

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16 pages, 3231 KiB  
Article
A Low-Resolution Infrared Array for Unobtrusive Human Activity Recognition That Preserves Privacy
by Nishat Tasnim Newaz and Eisuke Hanada
Sensors 2024, 24(3), 926; https://doi.org/10.3390/s24030926 - 31 Jan 2024
Viewed by 775
Abstract
This research uses a low-resolution infrared array sensor to address real-time human activity recognition while prioritizing the preservation of privacy. The proposed system captures thermal pixels that are represented as a human silhouette. With camera and image processing, it is easy to detect [...] Read more.
This research uses a low-resolution infrared array sensor to address real-time human activity recognition while prioritizing the preservation of privacy. The proposed system captures thermal pixels that are represented as a human silhouette. With camera and image processing, it is easy to detect human activity, but that reduces privacy. This work proposes a novel human activity recognition system that uses interpolation and mathematical measures that are unobtrusive and do not involve machine learning. The proposed method directly and efficiently recognizes multiple human states in a real-time environment. This work also demonstrates the accuracy of the outcomes for various scenarios using traditional ML approaches. This low-resolution IR array sensor is effective and would be useful for activity recognition in homes and healthcare centers. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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18 pages, 8565 KiB  
Article
IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection
by Jun Fan, Jingbiao Wei, Hai Huang, Dafeng Zhang and Ce Chen
Sensors 2023, 23(9), 4240; https://doi.org/10.3390/s23094240 - 24 Apr 2023
Cited by 4 | Viewed by 2225
Abstract
Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection [...] Read more.
Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection and tracking, comprising three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. We designed a CNN-based real-time detection model with a high recall rate for the first component to detect potential object regions in the entire image. The KCF algorithm and the designed lightweight CNN-based target detection model, which parallelly lock on the target more precisely in the target potential area, were used in the second component. In the final component, we designed an optimized Kalman filter to estimate the target’s trajectory. We validated our method on a public dataset. The results show that the proposed real-time detection and tracking framework for infrared vehicle small targets could steadily track vehicle targets and adapt well in situations such as the temporary disappearance of targets and interference from other vehicles. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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18 pages, 2213 KiB  
Article
Study on Rapid Detection of Pesticide Residues in Shanghaiqing Based on Analyzing Near-Infrared Microscopic Images
by Haoran Sun, Liguo Zhang, Lijun Ni, Zijun Zhu, Shaorong Luan and Ping Hu
Sensors 2023, 23(2), 983; https://doi.org/10.3390/s23020983 - 14 Jan 2023
Cited by 1 | Viewed by 2013
Abstract
Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of [...] Read more.
Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of Shanghaiqing (Brassica rapa), a type of Chinese cabbage with computer vision technology. After image pre-processing and feature extraction, the pattern recognition methods of K nearest neighbors (KNN), naïve Bayes, support vector machine (SVM), and back propagation artificial neural network (BP-ANN) were applied to assess whether Shanghaiqing is sprayed with pesticides. The SVM method with linear or RBF kernel provides the highest recognition accuracy of 96.96% for the samples sprayed with trichlorfon at a concentration of 1 g/L. The SVM method with RBF kernel has the highest recognition accuracy of 79.16~84.37% for the samples sprayed with cypermethrin at a concentration of 0.1 g/L. The investigation on the SVM classification models built on the samples sprayed with cypermethrin at different concentrations shows that the accuracy of the models increases with the pesticide concentrations. In addition, the relationship between the concentration of the cypermethrin sprayed and the image features was established by multiple regression to estimate the initial pesticide concentration on the Shanghaiqing leaves. A pesticide degradation equation was established on the basis of the first-order kinetic equation. The time for pesticides concentration to decrease to an acceptable level can be calculated on the basis of the degradation equation and the initial pesticide concentration. The present work provides a feasible way to rapidly detect pesticide residue on Shanghaiqing by means of NIR microscopic image technique. The methodology laid out in this research can be used as a reference for the pesticide detection of other types of vegetables. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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18 pages, 5011 KiB  
Article
IPD-Net: Infrared Pedestrian Detection Network via Adaptive Feature Extraction and Coordinate Information Fusion
by Lun Zhou, Song Gao, Simin Wang, Hengsheng Zhang, Ruochen Liu and Jiaming Liu
Sensors 2022, 22(22), 8966; https://doi.org/10.3390/s22228966 - 19 Nov 2022
Cited by 3 | Viewed by 1422
Abstract
Infrared pedestrian detection has important theoretical research value and a wide range of application scenarios. Because of its special imaging method, infrared images can be used for pedestrian detection at night and in severe weather conditions. However, the lack of pedestrian feature information [...] Read more.
Infrared pedestrian detection has important theoretical research value and a wide range of application scenarios. Because of its special imaging method, infrared images can be used for pedestrian detection at night and in severe weather conditions. However, the lack of pedestrian feature information in infrared images and the small scale of pedestrian objects makes it difficult for detection networks to extract feature information and accurately detect small-scale pedestrians. To address these issues, this paper proposes an infrared pedestrian detection network based on YOLOv5, named IPD-Net. Firstly, an adaptive feature extraction module (AFEM) is designed in the backbone network section, in which a residual structure with stepwise selective kernel was included to enable the model to better extract feature information under different sizes of the receptive field. Secondly, a coordinate attention feature pyramid network (CA-FPN) is designed to enhance the deep feature map with location information through the coordinate attention module, so that the network gains better capability of object localization. Finally, shallow information is introduced into the feature fusion network to improve the detection accuracy of weak and small objects. Experimental results on the large infrared image dataset ZUT show that the mean Average Precision (mAP50) of our model is improved by 3.6% compared to that of YOLOv5s. In addition, IPD-Net shows various degrees of accuracy improvement compared to other excellent methods. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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14 pages, 6238 KiB  
Article
Infrared Single-Frame Small Target Detection Based on Block-Matching
by Yi Man, Qingyun Yang and Tao Chen
Sensors 2022, 22(21), 8300; https://doi.org/10.3390/s22218300 - 29 Oct 2022
Cited by 4 | Viewed by 1335
Abstract
The robust detection of small targets is one of the crucial techniques in an infrared system. It is still a challenge to detect small targets under complex backgrounds. Aiming at the problem where infrared small target detection is easily disturbed by complex backgrounds, [...] Read more.
The robust detection of small targets is one of the crucial techniques in an infrared system. It is still a challenge to detect small targets under complex backgrounds. Aiming at the problem where infrared small target detection is easily disturbed by complex backgrounds, an infrared single frame detection method based on a block-matching approach is proposed in this paper. Firstly, the input infrared image is processed by extracting blocks from it. A new infrared model is constructed by finding blocks that are similar to each such block. Then, the small target detection based on the block-matching model is formulated as an optimization problem of recovering low-rank and sparse matrices, which are effectively solved using robust principal component analysis. Finally, the results of processing are reconstructed to obtain the target and background images. A simple segmentation method is used to segment the target image. The experimental results from the actual infrared sequences show that the proposed method has better background suppression ability under complex backgrounds and better detection performance than conventional baseline methods. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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Review

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32 pages, 1734 KiB  
Review
A Review of Machine Learning for Near-Infrared Spectroscopy
by Wenwen Zhang, Liyanaarachchi Chamara Kasun, Qi Jie Wang, Yuanjin Zheng and Zhiping Lin
Sensors 2022, 22(24), 9764; https://doi.org/10.3390/s22249764 - 13 Dec 2022
Cited by 27 | Viewed by 8569
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) [...] Read more.
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction. Full article
(This article belongs to the Special Issue Infrared Sensing and Target Detection)
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