Relevant bibliographies by topics / Face detection on thermal image

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Face detection on thermal image'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Face detection on thermal image"

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Seo, Jongwoo, and In-Jeong Chung. "Face Liveness Detection Using Thermal Face-CNN with External Knowledge." Symmetry 11, no. 3 (2019): 360. http://dx.doi.org/10.3390/sym11030360.

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Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP
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Hiroki, Ito Kosuke Oiwa and Akio Nozawa: Aoyama Gakuin University Japan. "Image Segmentation-Based Face Tracking on Thermal Images for Automatic Estimation of Psychophysiological States Using Facial Skin Temperature Distribution." Journal of Information Bioinformatics and Neuroscience (JBINS) Volume 4, Issue 1 (2020): 142–46. https://doi.org/10.5281/zenodo.4273823.

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Abstract: In human-machine system, human and machine need to recognize each other’s state with continuously, quantitatively and real-time property. Facial skin temperature could be measured with these properties by infrared thermography. The non-contact property is a great advantage in bioinstrumentation. Previous studies have been reported the availability of facial skin temperature for evaluation of psychophysiological states of a human such as stress, drowsiness and emotion. On the other hand, the development of the face detection and tracking techniques on thermal images are necessar
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Albar, Albar, Hendrick Hendrick, and Rahmad Hidayat. "Segmentation Method for Face Modelling in Thermal Images." Knowledge Engineering and Data Science 3, no. 2 (2020): 99. http://dx.doi.org/10.17977/um018v3i22020p99-105.

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Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epoc
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Pavez, Vicente, Gabriel Hermosilla, Manuel Silva, and Gonzalo Farias. "Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation." Mathematics 11, no. 21 (2023): 4446. http://dx.doi.org/10.3390/math11214446.

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In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal fa
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Ma, Chao, Ngo Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi. "Adapting Local Features for Face Detection in Thermal Image." Sensors 17, no. 12 (2017): 2741. http://dx.doi.org/10.3390/s17122741.

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Cho, Se, Na Baek, Min Kim, Ja Koo, Jong Kim, and Kang Park. "Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network." Sensors 18, no. 9 (2018): 2995. http://dx.doi.org/10.3390/s18092995.

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Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to dete
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Li, Shuoda, Xinyao Wang, and Xicai Li. "Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition." Journal of Physics: Conference Series 2868, no. 1 (2024): 012031. http://dx.doi.org/10.1088/1742-6596/2868/1/012031.

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Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delica
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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural ne
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Latinović, Nikola, Tijana Vuković, Ranko Petrović, et al. "Implementation challenge and analysis of thermal image degradation on R-CNN face detection." Telfor Journal 12, no. 2 (2020): 98–103. http://dx.doi.org/10.5937/telfor2002098l.

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Face detection systems with color cameras were rapidly evolving and have been well researched. In environments with good visibility they can reach excellent accuracy. But changes in illumination conditions can result in performance degradation, which is the one of the major limitations in visible light face detection systems. The solution to this problem could be in using thermal infrared cameras, since their operation doesn't depend on illumination. Recent studies have shown that deep learning methods can achieve an impressive performance on object detection tasks, and face detection in parti
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Trongtirakul, Thaweesak, Karen Panetta, Artyom M. Grigoryan, and Sos S. Agaian. "A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding." Entropy 27, no. 5 (2025): 526. https://doi.org/10.3390/e27050526.

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Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches, have shown potential in image segmentation, they encounter significant limitations when processing thermal images, such as poor spatial resolution, low contrast, lack of color and texture information, and susceptibility to noise and background clutter. This paper introduces a novel adaptive unsupervised entropy algorithm (A
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Dissertations / Theses on the topic "Face detection on thermal image"

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Ribeiro, Ricardo Ferreira. "Face detection on infrared thermal image." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23551.

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Mestrado em Engenharia Eletrónica e Telecomunicações<br>Infrared cameras or thermal imaging cameras are devices that use infrared radiation to capture an image. This kind of sensors are being developed for almost a century now. They started to be used in the military environment, but at that time it took too long to create a single image. Nowadays, the infrared sensors have reached a whole new technological level and are used for other than military purposes. These sensors are being used for face detection in this thesis. When comparing the use of thermal images regarding color images,
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Roman, Matej. "Automatizované měření teploty v boji proti COVID." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442439.

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This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software
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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.

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Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images
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Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.

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Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust
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Wall, Helene. "Context-Based Algorithm for Face Detection." Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4171.

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<p>Face detection has been a research area for more than ten years. It is a complex problem due to the high variability in faces and amongst faces; therefore it is not possible to extract a general pattern to be used for detection. This is what makes the face detection problem a challenge.</p><p>This thesis gives the reader a background to the face detection problem, where the two main approaches of the problem are described. A face detection algorithm is implemented using a context-based method in combination with an evolving neural network. The algorithm consists of two majors steps: detect
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Liang, Antoni. "Face Image Retrieval with Landmark Detection and Semantic Concepts Extraction." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/54081.

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This thesis proposes various novel approaches for improving the performances of automatic facial landmarks detection system based on the concept of pictorial tree structure model. Furthermore, a robust glasses landmark detection system is also proposed as glasses are commonly used. These proposed approaches are employed to develop an automatic semantic based face images retrieval system. The experiment results demonstrate significant improvements of all the proposed approaches towards accuracy and efficiency.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes
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Yigit, Ahmet. "Thermal And Visible Band Image Fusion For Abandoned Object Detection." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611720/index.pdf.

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Packages that are left unattended in public spaces are a security concern and timely detection of these packages is important for prevention of potential threats. Operators should be always alert to detect abandoned items in crowded environments. However, it is very difficult for operators to stay concentrated for extended periods. Therefore, it is important to aid operators with automatic detection of abandoned items. Most of the methods in the literature define abandoned items as items newly added to the scene and stayed stationary for a predefined time. Hence other stationary objects, such
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Rondahl, Thomas. "Face Detection in Digital Imagery Using Computer Vision and Image Processing." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51406.

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By adding a failure fault limit to an existing implementation of a face detection system application and a tolerance limit for detection time, a desired throughput for detected objects could be established. The aim of this thesis was to add an increased detection rate for pro le/partial faces while increasing the stability and run-time of the system. The results were obtained through an empirical analysis of test data which was compared between the implementation done for this thesis and the older implementation. The results showed an increase in detected faces (in low sized images) by 10% whi
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Books on the topic "Face detection on thermal image"

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Kawulok, Michal, M. Emre Celebi, and Bogdan Smolka, eds. Advances in Face Detection and Facial Image Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25958-1.

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1950-, Ahuja Narendra, ed. Face detection and gesture recognition for human-computer interaction. Kluwer Academic, 2001.

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Yang, Ming-Hsuan. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001.

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Celebi, M. Emre, Bogdan Smolka, and Michal Kawulok. Advances in Face Detection and Facial Image Analysis. Springer International Publishing AG, 2016.

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Smolka, Bogdan, Emre Celebi, and Michal Kawulok. Advances in Face Detection and Facial Image Analysis. Springer, 2016.

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Smolka, Bogdan, Emre Celebi, and Michal Kawulok. Advances in Face Detection and Facial Image Analysis. Springer, 2018.

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Ahuja, Narendra, and Ming-Hsuan Yang. Face Detection and Gesture Recognition for Human-Computer Interaction (The International Series in Video Computing). Springer, 2001.

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Escriva, David Millan, Roy Shilkrot, Prateek Joshi, and Vinicius G. Mendonca. Building Computer Vision Projects with OpenCV 4 and C++: Implement complex computer vision algorithms and explore deep learning and face detection. Packt Publishing, 2019.

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Book chapters on the topic "Face detection on thermal image"

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Silva, Gustavo, Rui Monteiro, André Ferreira, Pedro Carvalho, and Luís Corte-Real. "Face Detection in Thermal Images with YOLOv3." In Advances in Visual Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_8.

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Chen, Yujia, Liqing Wang, and Guangda Xu. "Face Detection on Thermal Infrared Images Combined with Visible Images." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1253-5_26.

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Sandhya, B., D. V. S. R. Sesidhar, Lathasree Reddy, T. Meghana, and B. Sony. "Detection of Face Mask in Thermal Images Using Deep CNN." In Smart Intelligent Computing and Applications, Volume 2. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9705-0_15.

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Forczmański, Paweł. "Human Face Detection in Thermal Images Using an Ensemble of Cascading Classifiers." In Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48429-7_19.

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Kopaczka, Marcin, Özcan Özkan, and Dorit Merhof. "Face Tracking and Respiratory Signal Analysis for the Detection of Sleep Apnea in Thermal Infrared Videos with Head Movement." In New Trends in Image Analysis and Processing – ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70742-6_15.

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Majidpour, Jafar, Aram M. Ahmed, Bryar A. Hassan, et al. "Thermal Face Image Reidentification Based on Variational Autoencoder, Cascade Object Detector Using Lightweight Architectures." In Practical Artificial Intelligence for Internet of Medical Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003315476-15.

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Kopaczka, Marcin, Jan Nestler, and Dorit Merhof. "Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches." In Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_44.

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Choraś, Ryszard S. "Thermal Face Recognition." In Image Processing and Communications Challenges 7. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23814-2_5.

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Rihan, Jonathan, Pushmeet Kohli, and Philip H. S. Torr. "OBJCUT for Face Detection." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_51.

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Forczmański, Paweł, and Anton Smoliński. "Eyes State Detection in Thermal Imaging." In Image Processing and Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_4.

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Conference papers on the topic "Face detection on thermal image"

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Seong, Lee Jian, Sumendra Yogarayan, Siti Fatimah Abdul Razak, and Jashila Nair Mogan. "Drunk Detection Using Thermal-Based Face Images." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913145.

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Carnap, Jacob, Alexander Kurz, Olaf Henniger, and Arjan Kuijper. "Occlusion Detection for Face Image Quality Assessment." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013168500003905.

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Agarwal, Akshay, and Nalini Ratha. "Face Morphing Detection in Social Media Content." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10648209.

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Zhou, Zichang, Sheng Wen, Chen Zhang, and Yule Sun. "Face Age Detection Based on YOLOv8." In 2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA). IEEE, 2025. https://doi.org/10.1109/dipca65051.2025.11042314.

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Ma, Chao, Ngo Thanh Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi. "Mixed features for face detection in thermal image." In The International Conference on Quality Control by Artificial Vision 2017, edited by Hajime Nagahara, Kazunori Umeda, and Atsushi Yamashita. SPIE, 2017. http://dx.doi.org/10.1117/12.2266836.

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Wong, Wai Kit, Joe How Hui, Jalil Bin Md Desa, Nur Izzati Nadiah Binti Ishak, Azlan Bin Sulaiman, and Yante Binti Mohd Nor. "Face detection in thermal imaging using head curve geometry." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469684.

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Hendrick, Surfa Yondri, Rahmat Hidayat, Albar Albar, Hanifa Fitri, and Ivan Finiel Bagariang. "Face Detection Model for Thermal Images." In 2020 International Conference on Applied Science and Technology (iCAST). IEEE, 2020. http://dx.doi.org/10.1109/icast51016.2020.9557718.

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Hussien, M. Naeem, Mohd-Haris Lye, Mohammad Faizal Ahmad Fauzi, Tan Ching Seong, and Sarina Mansor. "Comparative analysis of eyes detection on face thermal images." In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. http://dx.doi.org/10.1109/icsipa.2017.8120641.

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Vukovic, Tijana, Ranko Petrovic, Milos Pavlovic, and Srdan Stankovic. "Thermal Image Degradation Influence on R-CNN Face Detection Performance." In 2019 27th Telecommunications Forum (TELFOR). IEEE, 2019. http://dx.doi.org/10.1109/telfor48224.2019.8971128.

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Kwaśniewska, A., and J. Rumiński. "Face detection in image sequences using a portable thermal camera." In 2016 Quantitative InfraRed Thermography. QIRT Council, 2016. http://dx.doi.org/10.21611/qirt.2016.071.

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Reports on the topic "Face detection on thermal image"

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Duberstein, Corey A., Shari Matzner, Valerie I. Cullinan, Daniel J. Virden, Joshua R. Myers, and Adam R. Maxwell. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1076723.

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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, th
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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmar
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s
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Clausen, Jay, Susan Frankenstein, Jason Dorvee, et al. Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41780.

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An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the mat
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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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Abstract:
The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da
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