A comparative study of attention mechanism and generative adversarial network in facade damage segmentation
This work addresses facade damage detection for building inspection, but it is incremental as it applies existing methods to a specific domain.
The paper compared attention mechanisms and generative adversarial networks for facade damage segmentation, finding that a combination of these strategies improved performance, with specific metrics like IoU scores showing gains.
Semantic segmentation profits from deep learning and has shown its possibilities in handling the graphical data from the on-site inspection. As a result, visual damage in the facade images should be detected. Attention mechanism and generative adversarial networks are two of the most popular strategies to improve the quality of semantic segmentation. With specific focuses on these two strategies, this paper adopts U-net, a representative convolutional neural network, as the primary network and presents a comparative study in two steps. First, cell images are utilized to respectively determine the most effective networks among the U-nets with attention mechanism or generative adversarial networks. Subsequently, selected networks from the first test and their combination are applied for facade damage segmentation to investigate the performances of these networks. Besides, the combined effect of the attention mechanism and the generative adversarial network is discovered and discussed.