Data Processing with Artificial Intelligence in Thermal Imagery

Dear Colleagues,

Thermal imaging possesses various advantages over the visible light spectrum, allowing us to not only address challenging lighting conditions (e.g., poor lighting [1]), but also reveal information invisible to the naked eye [2]. For this reason, this imaging domain is continuously gaining more popularity across a broad variety of markets, e.g., in the automotive industry for scene understanding [3] and driver monitoring [4]; in the medical field for evaluation of skin conditions [5] or vital sign extraction [6]; and for 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 the heat flow in objects, thermal images are more blurred with smooth borders between objects and there is an absence of high-frequency components such as edges and textures [10]; 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 warp speed, only a few studies have focused 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, which are 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 relationships 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 welcome, including, but not limited to:

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

Reference

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

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