A Comprehensive Survey on the Research and Development of RGB-T Salient Object Detection
Jia Hou, Hongfa Wen, Qiang Zhao, Junbo Ma, Zongpeng Li, Shuai Wang, Chenggang Yan · Computational Visual Media · 2025
Salient object detection (SOD) aims to mimic human visual perception by identifying the most eye-catching objects within a scene, and has remained a popular research topic for many years. The introduction of thermal (T) images offers additional information for challenging scenarios such as those with low light and complex backgrounds, and thus enhance performance when combined with RGB images. In this paper, we have, to the best of our ability, conducted the first comprehensive survey of dual-modality RGB-T SOD. We summarize and categorize published RGB-T SOD models, emphasizing their characteristics and features. Important components of these models are classified and elaborated, such as feature extraction, modality fusion, and loss function design. Following this, we analyze existing RGB-T SOD datasets and evaluation metrics. We evaluate a selection of representative SOD models using unified protocols and statistical analysis. We also present comparative experiments on the impact of the choice of loss function and the usage of datasets on performance. Finally, we consider several key issues and potential solutions in RGB-T SOD research, revealing promising directions for future efforts. We hope this survey will offer an effective way to understand the current state of the technology and, more importantly, stimulate discussion within the community.