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Open AccessArticle

Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks

1
Department of Software Science, Tallinn University of Technology, 19086 Tallinn, Estonia
2
Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(8), 198; https://doi.org/10.3390/a13080198
Received: 30 June 2020 / Revised: 31 July 2020 / Accepted: 12 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure is considered, based on a pipeline of three ConvNets and endowed with the capacity for context awareness, which improves grid-based search for defects on orthoframes by considering the surrounding image content—an approach, which essentially draws inspiration from how humans tend to solve the task of image segmentation. Also, methods for assessing the quality of segmentation are discussed. The contribution also describes the complete procedure of working with pavement defects in an industrial setting, involving the workcycle of defect annotation, ConvNet training and validation. The results of ConvNet evaluation provided in the paper hint at a successful implementation of the proposed technique. View Full-Text
Keywords: pavement distress; digital image; Deep Learning; transfer learning; Convolutional Neural Network; active learning pavement distress; digital image; Deep Learning; transfer learning; Convolutional Neural Network; active learning
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Lõuk, R.; Riid, A.; Pihlak, R.; Tepljakov, A. Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks. Algorithms 2020, 13, 198.

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