Labeled Cracks in the Wild (LCW) Dataset

Labeled Cracks in the Wild (LCW) is a dataset which comprises of real images taken from Virginia Department of Transportation (VDOT) structural inspection reports. This dataset focuses on cracks in the global scene rather than zoomed-in concrete patch. The cracks for LCW were annotated using the GIMP software (The GIMP Development Team, 2019). The guidelines for the annotations are provided by the authors in the file folder. There are a total of 3,817 finely annotated images. The images were split into training and testing, 90% and 10% respectfully. The images were resized to 512x512 for training and testing the DeeplabV3+ model. The original and resized images are included. After training with the DeeplabV3+ model (DOI: 10.7294/16628707), we were able to correctly identify approximately 40% of the annotated ground truth cracks. More details of the training, the results, the dataset, and the code may be referenced in the journal article. The GitHub repository information may be found in the journal article.<br>If you are using the dataset in your work, please include <b>both </b>the journal article and the dataset citation. <br>

野外标注裂缝数据集（Labeled Cracks in the Wild, LCW）是一套源自弗吉尼亚州交通部（Virginia Department of Transportation, VDOT）结构检测报告的实拍图像数据集。该数据集聚焦于全局场景中的裂缝，而非局部特写的混凝土修补区域。LCW的裂缝标注采用GIMP图像处理软件（GIMP开发团队，2019）完成，标注指南由作者提供于数据集配套文件夹中。数据集共包含3817张精细标注的图像，并按90%训练集、10%测试集的比例划分为训练集与测试集。为训练与测试DeeplabV3+模型，所有图像均被调整至512×512分辨率，原始图像与调整后的图像均已包含在数据集中。
基于DeeplabV3+模型（DOI: 10.7294/16628707）完成训练后，我们可准确识别出约40%的基准标注裂缝。更多关于训练流程、实验结果、数据集详情与代码的相关信息，可查阅该期刊论文；GitHub仓库的相关信息亦刊载于该期刊论文中。
若您的研究工作中使用本数据集，请同时引用该期刊论文与本数据集的相关文献。