Dorafshan Sattar, Thomas Robert J, Maguire Marc
Department of Civil and Environmental Engineering, Utah State University, Logan, Utah. USA.
Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY, USA.
Data Brief. 2018 Nov 6;21:1664-1668. doi: 10.1016/j.dib.2018.11.015. eCollection 2018 Dec.
SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19.
SDNET2018是一个带注释的图像数据集，用于基于人工智能的混凝土裂缝检测算法的训练、验证和基准测试。SDNET2018包含超过56,000张有裂缝和无裂缝的混凝土桥面板、墙壁及路面的图像。该数据集包含宽度小至0.06毫米、大至25毫米的裂缝。数据集还包括带有各种障碍物的图像，如阴影、表面粗糙度、剥落、边缘、孔洞和背景碎片。SDNET2018将有助于基于深度卷积神经网络（DCNN）的混凝土裂缝检测算法的持续发展，这是结构健康监测领域持续研究的一个课题。作者展示了使用SDNET2018和基于AlexNet DCNN架构的裂缝检测算法进行裂缝检测的基准结果。SDNET2018可在https://doi.org/10.15142/T3TD19上免费获取。
