This application relates to the field of
artificial intelligence and discloses a method for extracting cultural elements based on intelligent recognition. It aims to address the problems of low segmentation accuracy, broken or adhered boundaries in cultural heritage images caused by
low contrast, complex structure, and scarce annotations. The method includes: acquiring a cultural image and generating an initial coarse
mask using pre-trained U-Net++; inputting the image and
mask into a conditional generator to generate a high-fidelity reconstructed image; calculating a local adversarial loss using a PatchGAN
discriminator; using the gradient of this loss to back-update the U-Net++ segmentation head parameters; iteratively optimizing until the
mask converges, and outputting a refined binary mask. This application, through a generative adversarial self-supervised mechanism, significantly reduces the dependence on newly added pixel-level annotations, guiding the model to learn continuous natural boundaries that conform to human
cognition, while requiring only a small number of pixel-level annotations for network initialization. The resulting mask can be directly used for vectorization, 3D reconstruction, and digital restoration, supporting high-fidelity digital applications of cultural heritage.