Thermal Semantic Segmentation

Thermal semantic segmentation aims to automatically classify and delineate objects in thermal infrared images, offering robust scene understanding in challenging lighting conditions where visible light imagery fails. Current research focuses on effectively fusing thermal data with complementary RGB information, employing architectures like encoder-decoder networks with attention mechanisms and specialized fusion modules to address the modality gap between the two image types. This field is crucial for advancing autonomous systems, particularly in robotics and autonomous driving, enabling reliable perception in low-light or adverse weather scenarios.