Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

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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Bibliographic Details
Main Authors: Rytis Augustauskas, Arūnas Lipnickas
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Sensors
Subjects:
CNN (Convolutional neural networks)
deep learning
pavement defects
residual connection
attention gate
atrous spatial pyramid pooling
Online Access:https://www.mdpi.com/1424-8220/20/9/2557

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https://www.mdpi.com/1424-8220/20/9/2557

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