Two-stage framework with improved U-Net based on self-supervised contrastive learning for pavement crack segmentation

Song, Qingsong; Yao, Wei; Tian, Haojiang; Guo, Yidan; Chandren Muniyandi, Ravie; An, Yisheng

doi:10.1016/j.eswa.2023.122406

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Application Of Gans In Defect Detection1

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2024

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Cited by 4 publications

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“…By using advanced GANs models, researchers can more accurately detect defects and thereby evaluate the reliability of components. This section will focus on how GANs were used in the detection of material defects [93–97] …”

Section: Application Of Gans In Defect Detectionmentioning

confidence: 99%

Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

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Generative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements.

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Section: Application Of Gans In Defect Detectionmentioning

confidence: 99%