Beskopylny Alexey N, Stel'makh Sergey A, Shcherban' Evgenii M, Razveeva Irina, Kozhakin Alexey, Meskhi Besarion, Chernil'nik Andrei, Elshaeva Diana, Ananova Oksana, Girya Mikhail, Nurkhabinov Timur, Beskopylny Nikita
Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia.
Department of Unique Buildings and Constructions Engineering, Don State Technical University, 344003 Rostov-on-Don, Russia.
Sensors (Basel). 2024 Jul 5;24(13):4373. doi: 10.3390/s24134373.
The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study's objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the "critical/uncritical" format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.
使用简单的人类视觉来寻找结构和微观结构缺陷，在确定混凝土微观和宏观结构中的空隙、大孔隙以及颗粒堆积的完整性和密实性是否遭到破坏时，会产生重大误差。计算机视觉方法，特别是卷积神经网络，已被证明是在建筑结构视觉检查中自动检测缺陷的可靠工具。该研究的目的是创建并比较使用卷积神经网络来识别和分析来自不同结构的混凝土样本中受损部分的计算机视觉算法。选择了以下架构的网络进行操作：U-Net、LinkNet和PSPNet。所分析的图像是通过实验室测试获得的混凝土样本照片，用于从结构完整性和密实性缺陷的角度评估质量。在实施过程中，监测了宏观平均精度、召回率、F1分数等质量指标的变化，以及交并比（Jaccard系数）和准确率。U-Net模型结合元胞自动机算法表现出了最佳指标：精度 = 0.91，召回率 = 0.90，F1 = 0.91，交并比 = 0.84，准确率 = 0.90。所开发的分割算法具有通用性，在任何拍摄条件和不同体积的缺陷区域下，无论其位置如何，都能高质量地突出感兴趣的区域。计算损伤面积过程的自动化以及“关键/非关键”格式的建议可用于评估各类结构混凝土的状况、调整配方并改变生产工艺参数。
