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Semantic segmentation profits from deep learning and has shown itspossibilities in handling the graphical data from the on-site inspection. As aresult, visual damage in the facade images should be detected. Attentionmechanism and generative adversarial networks are two of the most popularstrategies to improve the quality of semantic segmentation. With specificfocuses on these two strategies, this paper adopts U-net, a representativeconvolutional neural network, as the primary network and presents a comparativestudy in two steps. First, cell images are utilized to respectively determinethe most effective networks among the U-nets with attention mechanism orgenerative adversarial networks. Subsequently, selected networks from the firsttest and their combination are applied for facade damage segmentation toinvestigate the performances of these networks. Besides, the combined effect ofthe attention mechanism and the generative adversarial network is discoveredand discussed.

A comparative study of attention mechanism and generative adversarial network in facade damage segmentation