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Benefits of ensemble models in road pavement cracking classification
Author(s) -
RodriguezLozano Francisco J.,
LeónGarcía Fernando,
GámezGranados Juan C.,
Palomares Jose M.,
Olivares J.
Publication year - 2020
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12543
Subject(s) - task (project management) , computer science , pothole (geology) , segmentation , artificial intelligence , ensemble learning , ensemble forecasting , recall rate , cracking , machine learning , data mining , engineering , geology , petrology , chemistry , systems engineering
The maintenance of road pavements is an essential task to prevent major deterioration and to reduce accident rates. In this task, the detection and classification of different types of cracks on the roads is usually considered. However, in most cases, these tasks are not fully automated and they need to be supervised by an expert to make repair decisions. This work focuses on the automatic classification of the most common types of cracks: longitudinal cracks, transverse cracks, and alligator cracks. Our proposal combines, first, computer vision techniques for crack segmentation and second, an ensemble model (composed of different rule‐based algorithms) for the classification. This approach achieves an average precision and recall values greater than 94% for three analyzed data sets improving the results in comparison to other approaches.