Archaeology drawing generation algorithm based on multi-branch feature cross fusion

Abstract Archaeology drawing is a graphic recording technique that delineates the shape, structure, and ornamentation of cultural relics with lines, serving as a vital material in archaeological work and scholarly research. Aiming at the problems of low line accuracy and serious disease interference in the results of current mainstream image generation algorithms, this paper proposed an archaeology drawing generation algorithm based on multi-branch feature cross fusion (U2FGAN). The algorithm optimized skip connections in U2Net through channel attention mechanism, thereby constructing a multi-branch generator consisting of a line extractor and an edge detector, which separately identified line features and edge information within relic images before fusing them to generate accurate high-resolution line drawings. Additionally, a multi-scale conditional discriminator was incorporated to guide the generator towards outputting high-quality line drawings with clear details and intact structures. Experiments conducted on the Dunhuang mural line drawing datasets demonstrate that compared to mainstream counterparts, U2FGAN achieved a reduction in Mean Absolute Error (MAE) by 10.8–26.2%, while also exhibiting substantial improvements in Precision (by 9.8–32.3%), Fβ-Score (by 5.1–32%), and PSNR (by 0.4 to 2.2 dB). Experimental results show that the proposed method outperforms other mainstream algorithms in the task of archaeological line drawing generation.