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Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder
Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and ti...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249178/ https://www.ncbi.nlm.nih.gov/pubmed/32365925 http://dx.doi.org/10.3390/s20092557 |
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author | Augustauskas, Rytis Lipnickas, Arūnas |
author_facet | Augustauskas, Rytis Lipnickas, Arūnas |
author_sort | Augustauskas, Rytis |
collection | PubMed |
description | Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets. |
format | Online Article Text |
id | pubmed-7249178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72491782020-06-10 Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder Augustauskas, Rytis Lipnickas, Arūnas Sensors (Basel) Article Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets. MDPI 2020-04-30 /pmc/articles/PMC7249178/ /pubmed/32365925 http://dx.doi.org/10.3390/s20092557 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Augustauskas, Rytis Lipnickas, Arūnas Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title | Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title_full | Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title_fullStr | Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title_full_unstemmed | Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title_short | Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder |
title_sort | improved pixel-level pavement-defect segmentation using a deep autoencoder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249178/ https://www.ncbi.nlm.nih.gov/pubmed/32365925 http://dx.doi.org/10.3390/s20092557 |
work_keys_str_mv | AT augustauskasrytis improvedpixellevelpavementdefectsegmentationusingadeepautoencoder AT lipnickasarunas improvedpixellevelpavementdefectsegmentationusingadeepautoencoder |