Most recent methods for RGB (red–green–blue)-thermal salient object detection (SOD)
involve several floating-point operations and have numerous parameters, resulting in slow …

Robots can be equipped with a range of senses to allow them to perceive and interact with
the world in a more natural and intuitive way. These senses can include vision, hearing …

Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest.
However, most existing approaches to RGB-T urban scene parsing do not deeply explore …

Recently, RGB-Thermal based perception has shown significant advances. Thermal
information provides useful clues when visual cameras suffer from poor lighting conditions …

The semantic segmentation of road scenes is an important task in autonomous driving.
Deep learning has enabled the development of a variety of semantic segmentation networks …

The acquisition of high-resolution satellite and airborne remote sensing images has been
significantly simplified due to the rapid development of sensor technology. Several practical …

In recent years, computer vision systems have been increasingly applied to rail defect
inspection. Rail defects should be identified quickly and accurately to ensure safe, stable …

Understanding indoor scenes is crucial for urban studies. Considering the dynamic nature of
indoor environments effective semantic segmentation requires both real-time operation and …

In several existing red–green–blue and depth (RGB-D) semantic segmentation algorithms,
schemes are used to supplement contextual information through multilayer feature …

Abstract Recently, red-green-blue (RGB) and thermal (RGB-T) data have attracted
considerable interest for semantic segmentation because they provide robust imaging under …