Pavement crack detection algorithm based on generative adversarial network and convolutional neural network under small samples

Xu, Boqiang; Liu, Chao

doi:10.1016/j.measurement.2022.111219

Cited by 49 publications

(10 citation statements)

References 34 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%

Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

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show abstract

“…In the latest study, Xu et al [108] proposed a method to detect pavement cracks under small samples. Firstly, the image generated by GAN model is used to expand the original small sample dataset, and convolutional neural network (CNN) model is constructed at the same time.…”

Section: Overview Of Gan-based Construction Road Defect Detection App...mentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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“…GAN generates images with higher speed, higher resolution, and better performance than other algorithms do. Therefore, GAN has potential to be tapped for industrial defect image generation [ 14 ]. It has achieved remarkable achievements in the generation of natural landscape images and face images [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Steel Strip Defect Sample Generation Method Based on Fusible Feature GAN Model under Few Samples

Chen

2023

Sensors

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Due to the shortage of defect samples and the high cost of labelling during the process of hot-rolled strip production in the metallurgical industry, it is difficult to obtain a large quantity of defect data with diversity, which seriously affects the identification accuracy of different types of defects on the steel surface. To address the problem of insufficient defect sample data in the task of strip steel defect identification and classification, this paper proposes the Strip Steel Surface Defect-ConSinGAN (SDE-ConSinGAN) model for strip steel defect identification which is based on a single-image model trained by the generative adversarial network (GAN) and which builds a framework of image-feature cutting and splicing. The model aims to reduce training time by dynamically adjusting the number of iterations for different training stages. The detailed defect features of training samples are highlighted by introducing a new size-adjustment function and increasing the channel attention mechanism. In addition, real image features will be cut and synthesized to obtain new images with multiple defect features for training. The emergence of new images is able to richen generated samples. Eventually, the generated simulated samples can be directly used in deep-learning-based automatic classification of surface defects in cold-rolled thin strips. The experimental results show that, when SDE-ConSinGAN is used to enrich the image dataset, the generated defect images have higher quality and more diversity than the current methods do.

show abstract

Pavement crack detection algorithm based on generative adversarial network and convolutional neural network under small samples

Cited by 49 publications

References 34 publications

Applications of generative adversarial networks in materials science

Applications of generative adversarial networks in materials science

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Steel Strip Defect Sample Generation Method Based on Fusible Feature GAN Model under Few Samples

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