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.2