CODEBRIM: COncrete DEfect BRidge IMage Dataset

CODEBRIM: COncrete DEfect BRidge IMage Dataset for multi-target multi-class concrete defect classification in computer vision and machine learning. Dataset as presented and detailed in our CVPR 2019 publication: http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html or https://arxiv.org/abs/1904.08486 . If you make use of the dataset please cite it as follows: "Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019" We offer a supplementary GitHub repository with code to reproduce the paper and data loaders: https://github.com/ccc-frankfurt/meta-learning-CODEBRIM For ease of use we provide the dataset in multiple different versions. Files contained: * CODEBRIM_original_images: contains the original full-resolution images and bounding box annotations * CODEBRIM_cropped_dataset: contains the extracted crops/patches with corresponding class labels from the bounding boxes * CODEBRIM_classification_dataset: contains the cropped patches with corresponding class labels split into training, validation and test sets for machine learning * CODEBRIM_classification_balanced_dataset: similar to "CODEBRIM_classification_dataset" but with the exact replication of training images to balance the dataset in order to reproduce results obtained in the paper.CODEBRIM：面向计算机视觉与机器学习领域多目标多分类混凝土缺陷分类任务的混凝土缺陷桥梁图像数据集（COncrete DEfect BRidge IMage Dataset）。 本数据集的详细信息详见我们发表于2019年IEEE计算机视觉与模式识别会议（Conference on Computer Vision and Pattern Recognition，CVPR）的论文：http://openaccess.thecvf.com/content_CVPR_2019/html/Mundt_Meta-Learning_Convolutional_Neural_Architectures_for_Multi-Target_Concrete_Defect_Classification_With_CVPR_2019_paper.html 或 https://arxiv.org/abs/1904.08486。若使用本数据集，请按以下格式引用： "Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh. Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019" 我们提供了配套的GitHub开源仓库，其中包含复现论文所需代码与数据加载器：https://github.com/ccc-frankfurt/meta-learning-CODEBRIM。 为便于使用，我们提供了多种不同版本的数据集。 包含的文件如下： * CODEBRIM_original_images：存储原始高分辨率图像与边界框标注 * CODEBRIM_cropped_dataset：存储从边界框中提取的裁剪图像块及其对应类别标签 * CODEBRIM_classification_dataset：存储已划分为训练集、验证集与测试集的裁剪图像块及对应类别标签，可直接用于机器学习分类任务 * CODEBRIM_classification_balanced_dataset：与"CODEBRIM_classification_dataset"结构一致，但通过精确复制训练集图像实现数据集平衡，以复现论文中获得的实验结果。

搜集汇总

背景与挑战

背景概述

CODEBRIM数据集是一个专门用于计算机视觉和机器学习中多目标多类混凝土缺陷分类的图像数据集，主要针对桥梁结构。数据集包含原始图像、裁剪后的图像块以及分类数据集等多种版本，总大小约36.4 GB，便于研究人员直接使用。该数据集与CVPR 2019论文相关，要求用户在使用时按指定格式引用，并提供了GitHub代码库以支持复现实验。