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.

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