Open-source deep learning-based air-void detection algorithm for concrete microscopic images

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.