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
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 9; p. 2557
Main Authors: Augustauskas, Rytis, Lipnickas, Arūnas
Format: Journal Article
Language:English
Published: Switzerland MDPI 30.04.2020
MDPI AG
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20092557