Abstract
Dental caries is a prevalent disease of the human oral cavity. Given the lack of research on digital images for caries detection, we construct a caries detection dataset based on the caries images annotated by professional dentists and propose RDFNet, a fast caries detection method for the requirement of detecting caries on portable devices. The method incorporates the transformer mechanism in the backbone network for feature extraction, which improves the accuracy of caries detection and uses the FReLU activation function for activating visual-spatial information to improve the speed of caries detection. The experimental results on the image dataset constructed in this study show that the accuracy and speed of the method for caries detection are improved compared with the existing methods, achieving a good balance in accuracy and speed of caries detection, which can be applied to smart portable devices to facilitate human dental health management.