Thermal Image
Thermal imaging, capturing infrared radiation emitted by objects, offers valuable information complementary to visible light imagery, particularly in challenging conditions like low light or adverse weather. Current research focuses on improving thermal image analysis through deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs) for tasks such as object detection, scene reconstruction (including novel view synthesis using NeRFs), and image super-resolution. These advancements are significantly impacting diverse fields, including infrastructure inspection, autonomous driving, medical diagnostics, and agricultural monitoring, by enabling more robust and efficient solutions for various applications.
Papers
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
Francis Ogoke, Peter Myung-Won Pak, Alexander Myers, Guadalupe Quirarte, Jack Beuth, Jonathan Malen, Amir Barati Farimani
UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning
Maoxun Yuan, Bo Cui, Tianyi Zhao, Jiayi Wang, Shan Fu, Xingxing Wei
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
Connor Lee, Saraswati Soedarmadji, Matthew Anderson, Anthony J. Clark, Soon-Jo Chung
Leveraging Thermal Modality to Enhance Reconstruction in Low-Light Conditions
Jiacong Xu, Mingqian Liao, K Ram Prabhakar, Vishal M. Patel