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Keywords = orthoframes

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27 pages, 14281 KiB  
Article
Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
by Roland Lõuk, Andri Riid, René Pihlak and Aleksei Tepljakov
Algorithms 2020, 13(8), 198; https://doi.org/10.3390/a13080198 - 14 Aug 2020
Cited by 11 | Viewed by 4179
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms for Image Processing)
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22 pages, 16102 KiB  
Article
Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System
by Andri Riid, Roland Lõuk, Rene Pihlak, Aleksei Tepljakov and Kristina Vassiljeva
Appl. Sci. 2019, 9(22), 4829; https://doi.org/10.3390/app9224829 - 11 Nov 2019
Cited by 30 | Viewed by 5462
Abstract
The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in [...] Read more.
The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector. Full article
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7921 KiB  
Article
The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project
by Rosa Meo, Elena Roglia and Andrea Bottino
Sensors 2012, 12(12), 17504-17535; https://doi.org/10.3390/s121217504 - 17 Dec 2012
Cited by 13 | Viewed by 10682
Abstract
The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Italy 2012)
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