2023
DOI: 10.1098/rsta.2022.0166
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Automatic pavement texture recognition using lightweight few-shot learning

Abstract: Texture is a crucial characteristic of roads, closely related to their performance. The recognition of pavement texture is of great significance for road maintenance professionals to detect potential safety hazards and carry out necessary countermeasures. Although deep learning models have been applied for recognition, the scarcity of data has always been a limitation. To address this issue, this paper proposes a few-shot learning model based on the Siamese network for pavement texture recognition with a limit… Show more

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Cited by 7 publications
(2 citation statements)
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“…A model based on the nonlinear fitting was first presented to estimate the dielectric loss factor, and another prediction model of the dielectric constant of asphalt mixtures considering the temperature impact was proposed afterwards. Pan et al [7] conducted an automatic pavement texture recognition using lightweight few-shot learning. Their study proposed a few-shot learning model based on a Siamese network for pavement texture recognition with a limited dataset.…”
mentioning
confidence: 99%
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“…A model based on the nonlinear fitting was first presented to estimate the dielectric loss factor, and another prediction model of the dielectric constant of asphalt mixtures considering the temperature impact was proposed afterwards. Pan et al [7] conducted an automatic pavement texture recognition using lightweight few-shot learning. Their study proposed a few-shot learning model based on a Siamese network for pavement texture recognition with a limited dataset.…”
mentioning
confidence: 99%
“…Pan et al . [ 7 ] conducted an automatic pavement texture recognition using lightweight few-shot learning. Their study proposed a few-shot learning model based on a Siamese network for pavement texture recognition with a limited dataset.…”
mentioning
confidence: 99%