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Open AccessArticle

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

1
Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia
2
Department of Computer Systems, Tallinn University of Technology, 12618 Tallinn, Estonia
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper entitled “Deep Learning for Detection of Pavement Distress using Nonideal Photographic Images” and published in the Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) [2].
Appl. Sci. 2019, 9(22), 4829; https://doi.org/10.3390/app9224829
Received: 15 October 2019 / Revised: 5 November 2019 / Accepted: 6 November 2019 / Published: 11 November 2019
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. View Full-Text
Keywords: pavement distress; defect detection; image recognition; image processing; deep neural network pavement distress; defect detection; image recognition; image processing; deep neural network
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Riid, A.; Lõuk, R.; Pihlak, R.; Tepljakov, A.; Vassiljeva, K. Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System. Appl. Sci. 2019, 9, 4829.

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