Hilloulin B, Bekrine I, Schmitt E, Loukili A
Institut de Recherche en Génie Civil et Mécanique (GeM), UMR-CNRS 6183, Ecole Centrale de Nantes, Nantes, France.
Vicat, L'Isle d'Abeau, France.
J Microsc. 2022 May;286(2):179-184. doi: 10.1111/jmi.13098. Epub 2022 Mar 23.
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.
由于混凝土微观图像的高度异质组成和所涉及的不同尺度，分析混凝土微观图像具有一定难度。本文提出了一种基于深度学习的开源算法，专门用于检测混凝土微观图像中的气孔。该模型基于Mask R-CNN模型构建，其策略与混凝土成分信息一同呈现。随后讨论了模型性能，并与手动气孔增强技术进行了比较。最后，介绍了所选的开源策略。总体而言，该模型显示出良好的精度（平均精度均值mAP = 0.6452），预测的气孔百分比与实验测量结果相符，凸显了该模型未来评估混凝土耐久性的潜力。
