Thermal Imaging

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Thermal imaging studies how infrared measurements can reveal temperature, structure, motion, and material properties when visible-light cameras are limited or privacy is important. Methods range from radiometric calibration and heat-transfer inversion to deep learning for detection, segmentation, denoising, and super-resolution; RGB-thermal fusion, vision-language models, and thermal SLAM extend these capabilities to reasoning and 3D mapping. Applications include wildfire monitoring, industrial inspection, medical sensing, and human perception, evaluated on settings such as FLIR and RGBT-Scenes as well as radiometric UAV and active-thermography data. A central challenge is making results reliable across sensors and environments: models must distinguish true temperature evidence from emissivity, atmospheric, and imaging effects, while paired data and temperature-grounded model reasoning remain limited.

Latest papers

77 papers14 papers in the last four weeks, up 56% on the four weeks before. 0.1% of all new papers.

Multimodal Deep Learning for Diabetic Foot Ulcer Staging Using Integrated RGB and Thermal Imaging
Mar 27, 2026Gulengul Mermer, Mustafa Furkan Aksu, Gozde Ozsezer +3Multimodal Classification Thermal Imaging
TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock
Mar 27, 2026Taminul Islam, Abdellah Lakhssassi, Toqi Tahamid Sarker +3Agricultural Image Analysis Thermal Imaging
Southern Illinois University, Carbondale · University of California, Davis
Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis
Mar 20, 2026M. Kerem Aydin, Vishwanath Saragadam, Emma AlexanderNovel View Synthesis Thermal Imaging
Northwestern University · University of California, Riverside
TRGS-SLAM: IMU-Aided Gaussian Splatting SLAM for Blurry, Rolling Shutter, and Noisy Thermal Images
Mar 20, 2026Spencer Carmichael, Katherine A. SkinnerSimultaneous Localization and Mapping 3DGS SLAM
University of Michigan, Ann Arbor, MI USA
Thermal Image Refinement with Depth Estimation using Recurrent Networks for Monocular ORB-SLAM3
Mar 16, 2026Hürkan Şahin, Huy Xuan Pham, Van Huyen Dang +2Depth Estimation Monocular Depth Estimation
Automatic Control Group (RAT), Paderborn University, 33098 Paderborn, Germany · Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus C, Denmark, and also with Upteko ApS, Denmark
Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection
Mar 13, 2026Eman Ouda, Mohammed Salah, Arsenii O. Chulkov +4Thermal Imaging Anomaly Detection
Khalifa University of Science and Technology, Department of Aerospace Engineering, Abu Dhabi, United Arab Emirates · National Research Tomsk Polytechnic University, Russia · Academy of Fine Arts of Naples, Naples, Italy +3
Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
Mar 11, 2026Tao Zhong, Yixun Hu, Dongzhe Zheng +2PDE Inverse Problems Thermal Imaging
Princeton University
Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues
Mar 11, 2026Mohammed Salah, Eman Ouda, Giuseppe Dell'Avvocato +6Fault Detection Thermal Imaging
Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset
Mar 5, 2026Yang Zou, Jun Ma, Zhidong Jiao +3Remote Sensing Infrared Image Super-Resolution Autoregressive Image Super-Resolution
School of Computer Science, Northwestern Polytechnical University · School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology · School of Computer Science, Zhejiang University +1
Ozone Cues Mitigate Reflected Downwelling Radiance in LWIR Absorption-Based Ranging
Feb 27, 2026Unay Dorken Gallastegi, Wentao Shangguan, Vaibhav Choudhary +4Remote Sensing Thermal Imaging
Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215 USA · Boston University · Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia +1
TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection
Feb 25, 2026Alexis Apostolakis, Vasileios Botsos, Niklas Wölki +4Remote Sensing Image Segmentation Remote Sensing Benchmarks
National Technical University of Athens, School of Electrical and Computer Engineering · National Observatory of Athens · School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens +1
LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments
Feb 24, 2026Zeyu Jiang, Kuan Xu, Changhao ChenDynamic SLAM Visual SLAM
PEAK-Lab, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation
Feb 23, 2026Dong-Guw Lee, Tai Hyoung Rhee, Hyunsoo Jang +3Image-to-Image Translation Thermal Imaging
KENTECH
ThermEval: A Structured Benchmark for Evaluation of Vision-Language Models on Thermal Imagery
Feb 16, 2026Ayush Shrivastava, Kirtan Gangani, Laksh Jain +2VLM Evaluation Thermal Imaging
Indian Institute of Technology, Gandhinagar, India · Carnegie Mellon University, Pittsburgh, USA
Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring
Feb 12, 2026Constantino Álvarez Casado, Mohammad Rahman, Sasan Sharifipour +4Thermal Imaging Heart Rate Estimation
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland · Candour Ltd, Oulu, Finland
ThermoSplat: Cross-Modal 3D Gaussian Splatting with Feature Modulation and Geometry Decoupling
Jan 22, 2026Zhaoqi Su, Shihai Chen, Xinyan Lin +3Thermal Imaging 3D Gaussian Splatting
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management
Dec 3, 2024Bryce Hopkins, Leo ONeill, Michael Marinaccio +7Thermal Imaging Remote Sensing
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA · Pacific Southwest Research Station, U.S. Forest Services, Redding, CA USA · School of Environmental and Forest Sciences, University of Washington, Seattle, WA USA +3

Mar 27, 2026J/K move · Enter open · S save

Multimodal Deep Learning for Diabetic Foot Ulcer Staging Using Integrated RGB and Thermal Imaging

Gulengul Mermer, Mustafa Furkan Aksu, Gozde Ozsezer, Sevki Cetinkalp+2

Multimodal Classification Thermal Imaging

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Diabetic foot ulcers (DFU) are one of the serious complications of diabetes that can lead to amputations and high healthcare costs. Regular monitoring and early diagnosis are critical for reducing the clinical burden and the risk of amputation. The aim of this study is to investigate the impact of using multimodal images on deep learning models for the classification of DFU stages. To this end, we developed a Raspberry Pi-based portable imaging system capable of simultaneously capturing RGB and thermal images. Using this prototype, a dataset consisting of 1,205 samples was collected in a hospital setting. The dataset was labeled by experts into six distinct stages. To evaluate the models performance, we prepared three different training sets: RGB-only, thermal-only, and RGB+Thermal (with the thermal image added as a fourth channel). We trained these training sets on the DenseNet121, EfficientNetV2, InceptionV3, ResNet50, and VGG16 models. The results show that the multimodal training dataset, in which RGB and thermal data are combined across four channels, outperforms single-modal approaches. The highest performance was observed in the VGG16 model trained on the RGB+Thermal dataset. The model achieved an accuracy of 93.25%, an F1-score of 92.53%, and an MCC of 91.03%. Grad-CAM heatmap visualizations demonstrated that the thermal channel helped the model focus on the correct location by highlighting temperature anomalies in the ulcer region, while the RGB channel supported the decision-making process with complementary structural and textural information.