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With the fast-growing GUI development workload in the Internet industry, somework on intelligent methods attempted to generate maintainable front-end codefrom UI screenshots. It can be more suitable for utilizing UI design draftsthat contain UI metadata. However, fragmented layers inevitably appear in theUI design drafts which greatly reduces the quality of code generation. None ofthe existing GUI automated techniques detects and merges the fragmented layersto improve the accessibility of generated code. In this paper, we propose UILayers Merger (UILM), a vision-based method, which can automatically detect andmerge fragmented layers into UI components. Our UILM contains Merging AreaDetector (MAD) and a layers merging algorithm. MAD incorporates the boundaryprior knowledge to accurately detect the boundaries of UI components. Then, thelayers merging algorithm can search out the associated layers within thecomponents' boundaries and merge them into a whole part. We present a dynamicdata augmentation approach to boost the performance of MAD. We also construct alarge-scale UI dataset for training the MAD and testing the performance ofUILM. The experiment shows that the proposed method outperforms the bestbaseline regarding merging area detection and achieves a decent accuracyregarding layers merging.

UI Layers Merger: Merging UI layers via Visual Learning and Boundary Prior