CCIC-O (Concrete Crack Images for Classification - Orientation)

CCIC-O (Concrete Crack Images for Classification - Orientation) is a derived crack dataset constructed from the base dataset introduced by Özgenel and Sorguç [1] and released by Özgenel as Concrete Crack Images for Classification (CCIC) [2]. It is designed for fine-grained ordinal evaluation by providing controlled variation in crack orientation. Source images are filtered through an automated image-processing pipeline to retain cases dominated by a single, approximately straight, edge-to-edge crack. The remaining images are aligned to a common horizontal reference orientation and then rotated in 10° increments over the full circle, yielding 36 orientation classes and around 36,000 images at 227 × 227 pixels. To avoid dark border artifacts introduced by rotation, images are reflectively padded before rotation and center-cropped back to the target resolution afterwards. [1] Özgenel, Ç.F., Sorguç, A.G. Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings. ISARC 2018, Berlin, 2018. [2] Özgenel, Ç.F. Concrete Crack Images for Classification. Mendeley Data, V2, 2019. DOI: 10.17632/5y9wdsg2zt.2CCIC-O（Concrete Crack Images for Classification - Orientation，面向分类的混凝土裂缝图像数据集-方向维度）是一款衍生裂缝数据集，其基础数据集由Özgenel与Sorguç提出[1]，并由Özgenel以《面向分类的混凝土裂缝图像数据集（CCIC）》之名发布[2]。该数据集通过可控调整裂缝朝向，旨在开展细粒度的序数评估任务。 源图像将通过自动化图像处理流水线进行筛选，仅保留以单条近似笔直的贯穿式裂缝为核心主体的样本。剩余图像将对齐至统一的水平参考朝向，随后以10°为增量完成全圆周旋转，最终生成36个朝向类别，总计约36000张分辨率为227×227像素的图像。为避免旋转过程中产生的深色边框伪影，图像将在旋转前进行反射填充，旋转完成后再通过中心裁剪恢复至目标分辨率。 [1] Özgenel ÇF, Sorguç AG. 预训练卷积神经网络在建筑裂缝检测中的性能对比 // 2018年国际自动化与机器人学会议（ISARC 2018），柏林，2018. [2] Özgenel ÇF. 面向分类的混凝土裂缝图像数据集. Mendeley Data, V2版, 2019. DOI: 10.17632/5y9wdsg2zt.2