SIA: RGB-T salient object detection network with salient-illumination awareness

RGB-thermal salient object detection (RGB-T SOD) has rapidly developed and achieved excellent detection results. Unfortunately, the significant impact of illumination on salient object detection has not yet been adequately addressed, which results in mediocre detection performance of current methods in variable illumination scenes. To overcome the influence of illumination in variable illumination scenes, we present an RGB-T salient object detection network with salient-illumination awareness. Firstly, we propose a salient-illumination awareness estimator (SIAE) to evaluate the illumination condition of the RGB-T images. To make the generated illumination representation more comprehensively measure the quality of the images affected by illumination, we use saliency supervision and illumination semantic complement module (ISCM) to add salient awareness and semantic information to the illumination representation, respectively. Then, under the guidance of illumination representation, we use an illumination perception fusion module (IPFM) to complete the fusion of visible light and thermal infrared modality and predict salient objects. Next, to verify the rationality of our design idea and the validity of our proposed method, we construct a variable illumination dataset VI-RGBT3500 for experimental verification. We conduct many experiments on the VI-RGBT3500 dataset as well as existing datasets. The experimental results show that our method can achieve excellent detection results in variable illumination scenes. Moreover, excellent detection results can also be achieved in normal illumination scenes. The dataset and code are available at: https://github.com/VDT-2048/SIA.