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...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 9; S. 2557
Hauptverfasser:	Augustauskas, Rytis, Lipnickas, Arūnas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI 30.04.2020 MDPI AG
Schlagworte:	atrous spatial pyramid pooling attention gate CNN (Convolutional neural networks) deep learning pavement defects residual connection deep learning pavement defects atrous spatial pyramid pooling CNN (Convolutional neural networks) residual connection attention gate
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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