Special Issue "Thermal Data Processing with Artificial Intelligence"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 2463

Special Issue Editors

SiMa Technologies Inc. 226 Airport Parkway, Suite 550 San Jose, CA 95110, USA
Interests: computer vision; artificial intelligence; biomedical engineering; remote healthcare; super resolution; convolutional neural networks; machine learning; edge processing
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Construction Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, Canada
Interests: computer vision; image processing; artificial intelligence; deep learning; medical imaging; thermal imaging; spectroscopy; virtual reality; data analytics and risk assessment; electronics/embedded systems
Special Issues, Collections and Topics in MDPI journals
Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: deep learning; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thermal imaging possesses various advantages over the visible light spectrum, allowing not only for addressing challenging lighting conditions (e.g., poor lighting [1]) but also revealing information invisible to a naked eye [2]. That is why this imaging domain is continuously gaining more popularity across a broad variety of markets, e.g., automotive, for scene understanding [3] and driver monitoring [4], medical, for evaluation of skin conditions [5] or vital signs extraction [6], smart vision in surveillance [7] and border control [8] applications, just to name a few.

At the same time, it is important to note that thermal imagery has different characteristics than visible light data [9]. First, due to a heat flow in objects, thermal images are more blurred with smooth borders between objects and the absence of high-frequency components like edges and textures [10], and frequently, the lack of color data also makes image processing more challenging [11]. Secondly, ranges of thermal sensors are usually shorter than in the case of standard cameras, allowing them to capture only close-proximity scenes. Finally, the resolution of such data is usually lower due to the higher cost of imaging sensors [12].

Although the research in artificial intelligence is progressing at a warp speed, only a few studies focus on imaging domains other than RGB. Furthermore, models are usually designed with visible light spectrum data in mind, assuming that high-frequency components are present in the input data, and then directly applied to other datasets. However, this frequently leads to worse accuracy [13,14], as such networks cannot capture specific data characteristics, e.g., more distant relations between object components in thermal images that require bigger receptive fields [15].

Taking this into account, this Special Issue focuses on increasing the community's awareness of the importance of thermal imagery, its benefits, and challenges, as well as the need for careful analysis and design of AI solutions with specific data domains in mind. Proposals addressing various research topics are welcomed, including but not limited to:

  • Thermal imaging applications in medicine, automotive, aerospace, robotics, surveillance, and other industries.
  • AI design for thermal imagery including Neural Architecture Search for domain-specific tasks.
  • Data translation between imaging domains.
  • Thermal data generation using AI.


  1. Usamentiaga, R., Venegas, P., Guerediaga, J., Vega, L., Molleda, J. and Bulnes, F.G., 2014. Infrared thermography for temperature measurement and non-destructive testing. Sensors, 14(7), pp.12305-12348.
  2. Kwasniewska, A., Ruminski, J. and Szankin, M., 2019. Improving accuracy of contactless respiratory rate estimation by enhancing thermal sequences with deep neural networks. Applied Sciences, 9(20), p.4405.
  3. Weinmann, M., Leitloff, J., Hoegner, L., Jutzi, B., Stilla, U. and Hinz, S., 2014. THERMAL 3D MAPPING FOR OBJECT DETECTION IN DYNAMIC SCENES. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2(1).
  4. Weiss, C., Kirmas, A., Lemcke, S., Böshagen, S., Walter, M., Eckstein, L. and Leonhardt, S., 2022. Head tracking in automotive environments for driver monitoring using a low resolution thermal camera. Vehicles, 4(1), pp.219-233.
  5. Renkielska, A., Kaczmarek, M., Nowakowski, A., Grudziński, J., Czapiewski, P., Krajewski, A. and Grobelny, I., 2014. Active dynamic infrared thermal imaging in burn depth evaluation. Journal of Burn Care & Research, 35(5), pp.e294-e303.
  6. Kwaśniewska, A., Rumiński, J. and Rad, P., 2017, July. Deep features class activation map for thermal face detection and tracking. In 2017 10Th international conference on human system interactions (HSI) (pp. 41-47). IEEE.
  7. Stypułkowski, K., Gołda, P., Lewczuk, K. and Tomaszewska, J., 2021. Monitoring system for railway infrastructure elements based on thermal imaging analysis. Sensors, 21(11), p.3819.
  8. Khaksari, K., Nguyen, T., Hill, B.Y., Quang, T., Perrault, J., Gorti, V., Malpani, R., Blick, E., Cano, T.G., Shadgan, B. and Gandjbakhche, A.H., 2021. Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19. Journal of Medical Imaging, 8(S1), p.010901.
  9. Kwasniewska, A., Ruminski, J., Szankin, M. and Kaczmarek, M., 2020. Super-resolved thermal imagery for high-accuracy facial areas detection and analysis. Engineering Applications of Artificial Intelligence, 87, p.103263.
  10. Baskaran, R., Møller, K., Wiil, U.K. and Brabrand, M., 2022. Using Facial Landmark Detection on Thermal Images as a Novel Prognostic Tool for Emergency Departments. Frontiers in artificial intelligence, 5.
  11. Głowacka, N. and Rumiński, J., 2021. Face with mask detection in thermal images using deep neural networks. Sensors, 21(19), p.6387.
  12. Zhou, H., Sun, M., Ren, X. and Wang, X., 2021. Visible-Thermal Image Object Detection via the Combination of Illumination Conditions and Temperature Information. Remote Sensing, 13(18), p.3656.
  13. Ramanagopal, M.S., Zhang, Z., Vasudevan, R. and Johnson-Roberson, M., 2020. Pixel-wise motion deblurring of thermal videos. arXiv preprint arXiv:2006.04973.
  14. Kwasniewska, Alicja, Maciej Szankin, Jacek Ruminski, Anthony Sarah, and David Gamba. "Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features with Transformers and Recursive Convolutional Models." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3857-3867. 2021.
  15. Szankin, M., Kwasniewska, A. and Ruminski, J., 2019, June. Influence of thermal imagery resolution on accuracy of deep learning based face recognition. In 2019 12th International Conference on Human System Interaction (HSI) (pp. 1-6). IEEE.

Dr. Alicja Kwasniewska
Dr. M. Hamed Mozaffari
Prof. Dr. Yudong Zhang
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. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (1 paper)

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22 pages, 2962 KiB  
Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging
J. Imaging 2022, 8(10), 279; https://doi.org/10.3390/jimaging8100279 - 12 Oct 2022
Viewed by 1959
This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas–Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art [...] Read more.
This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas–Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi–Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected. Full article
(This article belongs to the Special Issue Thermal Data Processing with Artificial Intelligence)
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