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Open‐source deep learning‐based air‐void detection algorithm for concrete microscopic images
Author(s) -
Hilloulin B.,
Bekrine I.,
Schmitt E.,
Loukili A.
Publication year - 2022
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.13098
Subject(s) - void (composites) , durability , open source , computer science , open air , materials science , cement , void ratio , porosity , algorithm , artificial intelligence , composite material , software , engineering , programming language , architectural engineering
Analysing concrete microscopic images is difficult because of its highly heterogeneous composition and the different scales involved. This article presents an open‐source deep learning‐based algorithm dedicated to air‐void detection in concrete microscopic images. The model, whose strategy is presented alongside concrete compositions information, is built using the Mask R‐CNN model. Model performances are then discussed and compared to the manual air‐void enhancement technique. Finally, the selected open‐source strategy is exposed. Overall, the model shows a good precision (mAP = 0.6452), and the predicted air void percentage agrees with experimental measurements highlighting the model's potential to assess concrete durability in the future.