SDNET2018: A concrete crack image dataset for machine learning applications

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 learning convolutional neural networks, which are a subject of continued research in the field of structural health monitoring. .jpe

SDNET2018是一款用于混凝土裂缝检测人工智能算法训练、验证与基准测试的带标注图像数据集。该数据集包含超过5.6万张涵盖带裂缝与无裂缝混凝土桥面、墙体及路面的图像，其收录的裂缝宽度范围为0.06毫米至25毫米。此外，数据集中还包含带有各类干扰因素的图像，涉及阴影、表面粗糙度、混凝土起皮、边缘痕迹、孔洞及背景杂物等。SDNET2018可助力基于深度学习卷积神经网络（Convolutional Neural Networks, CNN）的混凝土裂缝检测算法的持续研发，而这类神经网络是当前结构健康监测（Structural Health Monitoring, SHM）领域的重点研究方向之一。

搜集汇总

背景与挑战

背景概述

SDNET2018是一个包含超过56,000张混凝土裂缝和非裂缝图像的数据集，适用于机器学习算法的训练和验证。数据集覆盖多种裂缝宽度和复杂背景条件，旨在支持基于深度学习的裂缝检测技术研究。