Salient Target Detection in RGB-T Image based on Multi-level Semantic Information

The salient target detection aims to use models or algorithms to detect and segment the salient target regions in the image. As an image preprocessing step, The salient target detection has been widely researched and applied in computer vision fields such as image segmentation, image compression, object recognition, and image retrieval. In the face of complex environments, such as poor lighting conditions and cluttered backgrounds, saliency detection is still challenging. RGB-T complementary fusion for saliency detection has become a hot research direction to solve the above problems. Therefore, this paper makes full use of the advantages of RGB and thermal infrared images to complement each other, and proposes an end-to-end salient target detection network. To this end, the VGG16 backbone network is first used for rough feature extraction, and then the attention mechanism module is used to enhance and merge the high-level features of RGB and thermal infrared images to enrich semantic information. Then the DFPN module is designed to accumulate high-level features to obtain high-level semantic information, and at the same time, the high-level features integrate the semantic information of thermal infrared images. Then we designed the SIPM self-interactive pooling module to further refine the features so that it can adaptively handle multi-scale targets. Finally, we compared the proposed method with several other advanced methods on several public datasets, and the experimental results proved the effectiveness of our method.