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20 September 2020 A deep learning approach to crack detection on road surfaces
Roman Sizyakin, Viacheslav Voronin, Nikolay Gapon, Aleksandra Pižurica
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Proceedings Volume 11543, Artificial Intelligence and Machine Learning in Defense Applications II; 115430P (2020) https://doi.org/10.1117/12.2574131
Event: SPIE Security + Defence, 2020, Online Only
Abstract
Currently, modern achievements in the field of deep learning are increasingly being applied in practice. One of the practical uses of deep learning is to detect cracks on the surface of the roadway. The destruction of the roadway is the result of various factors: for example, the use of low-quality material, non-compliance with the standards of laying asphalt, external physical impact, etc. Detection of these damages in automatic mode with high speed and accuracy is an important and complex task. An effective solution to this problem can reduce the time of services that carry out the detection of damage and also increase the safety of road users. The main challenge for automatically detecting such damage, in most cases, is the complex structure of the roadway. To accurately detect this damage, we use U-Net. After that we improve the binary map with localized cracks from the U-Net neural network, using the morphological filtering. This solution allows localizing cracks with higher accuracy in comparison with traditional methods crack detection, as well as modern methods of deep learning. All experiments were performed using the publicly available CRACK500 dataset with examples of cracks and their binary maps.
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Roman Sizyakin, Viacheslav Voronin, Nikolay Gapon, and Aleksandra Pižurica "A deep learning approach to crack detection on road surfaces", Proc. SPIE 11543, Artificial Intelligence and Machine Learning in Defense Applications II, 115430P (20 September 2020); https://doi.org/10.1117/12.2574131
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KEYWORDS
Roads
Databases
Machine learning
Neural networks
Safety
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