Drone-Image Based Fast Crack Analysis Algorithm Using Machine Learning for Highway Pavements

Moon, Byungkyu; Lee, Hosin

doi:10.3390/engproc2022017015

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2024

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“…The collection of cracks is risky and expensive, and the quality of the gathered photos also vary in uniformity and high quality, which is a problem that needs to be resolved. However, the issue of the source of crack datasets is the foundation of training good algorithm models [3,4].…”

Section: Introductionmentioning

confidence: 99%

Crack image generation algorithm based on deep convolutional generative adversarial network

Zhang

Ming-chao²,

Wang³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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In order to improve the image quality of a specific class of crack images, as well as to solve the problems of insufficient size of the number of crack datasets and small number of complex crack images, a crack image generation model based on DCGAN (Deep Convolutional Generative Adversarial Network, DCGAN) is proposed, which has superior training stability and convergence speed. The experimental results show that DCGAN can generate a large number of real crack images with complex backgrounds more reliably than traditional image augmentation methods, effectively solving the problem of lack of crack images in special cases and greatly reducing the cost of crack image acquisition tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Crack image generation algorithm based on deep convolutional generative adversarial network

Zhang

Ming-chao²,

Wang³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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Overview Study of the Applications of Unmanned Aerial Vehicles in the Transportation Sector

Kiss,

Ballagi,

Kuczmann

2024

SMTS 2024

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Drone-Image Based Fast Crack Analysis Algorithm Using Machine Learning for Highway Pavements

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 2 publications

References 6 publications

Crack image generation algorithm based on deep convolutional generative adversarial network

Crack image generation algorithm based on deep convolutional generative adversarial network

Overview Study of the Applications of Unmanned Aerial Vehicles in the Transportation Sector

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