RGB-thermal salient object detection (RGB-T SOD) leverages complementary RGB and thermal modalities to improve robustness under challenging conditions such as low illumination, occlusion, and adverse weather. Despite rapid progress in this area, a comprehensive and up-to-date survey of methodological developments remains unavailable. This survey provides a systematic review of 91 RGB-T SOD studies published up to April, 2026, covering the evolution of the field from early machine learning approaches to recent deep learning-based methods. Particular attention is given to emerging technologies, including vision foundation models and diffusion models. Deep learning-based approaches are analyzed from both architectural and technological perspectives and are organized into three functional components: feature extraction, feature enhancement and fusion, and decoding strategies. To support objective comparison, we present a quantitative evaluation of 37 representative models and an attribute-based analysis of 25 methods, characterizing their behavior across diverse and challenging scenarios. Based on this analysis, we identify key limitations and distill major research trends, concluding that cross-modal knowledge transfer and the integration of large-scale multimodal models constitute promising directions for future RGB-T SOD research. All reviewed methods are systematically categorized and maintained in a publicly available GitHub repository https://github.com/LLiSJ-web/Awesome-Salient-Object-Detection/tree/main to support reproducibility and ongoing development.
原文链接
分享
引用
