  Salient object detection in complex scenes and environments is a challenging
research topic. Most works focus on RGB-based salient object detection, which
limits its performance of real-life applications when confronted with adverse
conditions such as dark environments and complex backgrounds. Taking advantage
of RGB and thermal infrared images becomes a new research direction for
detecting salient object in complex scenes recently, as thermal infrared
spectrum imaging provides the complementary information and has been applied to
many computer vision tasks. However, current research for RGBT salient object
detection is limited by the lack of a large-scale dataset and comprehensive
benchmark. This work contributes such a RGBT image dataset named VT5000,
including 5000 spatially aligned RGBT image pairs with ground truth
annotations. VT5000 has 11 challenges collected in different scenes and
environments for exploring the robustness of algorithms. With this dataset, we
propose a powerful baseline approach, which extracts multi-level features
within each modality and aggregates these features of all modalities with the
attention mechanism, for accurate RGBT salient object detection. Extensive
experiments show that the proposed baseline approach outperforms the
state-of-the-art methods on VT5000 dataset and other two public datasets. In
addition, we carry out a comprehensive analysis of different algorithms of RGBT
salient object detection on VT5000 dataset, and then make several valuable
conclusions and provide some potential research directions for RGBT salient
object detection.
