TherNet: Thermal Segmentation Network Harnessing Physical Properties.

Abstract

Precise segmentation of thermal infrared images is crucial in domains like surveillance, medical diagnostics, intelligent transportation, accurate guidance and remote sensing. However, current thermal segmentation methods often oversimplify by treating thermal images as grayscale, neglecting vital physical factors such as thermal imaging effects and material information, thereby constraining segmentation precision. To address these limitations, we propose TherNet, a novel thermal infrared segmentation framework integrating thermal imaging effects and material physical information. The study elucidates the impacts of object radiation, inter-object thermal exchange, atmospheric scattering, and camera thermal inertia on thermal infrared imaging, developing four modules to model or rectify these physical processes. To validate the proposed framework, two large-scale infrared datasets were created: TI-Cityscapes for multi-class semantic segmentation in traffic scenes (4,200 frames, 18 classes), and TBRSD for single-object blindroad segmentation (5,180 frames from a pedestrian perspective). The proposed methods achieved SoTA performance across three infrared semantic segmentation datasets and the blind road segmentation dataset, underscoring the pivotal role of leveraging physical properties. TherNet provides innovative perspectives and robust benchmarks for future developments in the domain.