Kaggle Link For Dataset: https://www.kaggle.com/aniruddhsharma/structural-defects-network-concrete-crack-images
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.
230 images of cracked and non-cracked concrete surfaces (54 bridge decks, 72 walls, 104 pavements) are captured using a 16 MP Nikon digital camera. The bridge decks were located at the Utah State University system, material, and structural health (SMASH) laboratory. The inspected walls belong to Russell/Wanlass Performance Hall building located on USU campus The pavement images were acquired from the roads and sidewalks on USU campus. Each image is segmented into 256 × 256 px sub-images. Each sub-image is labelled as 'Cracked' if there was a crack in the sub-image or 'Non-cracked' if there was not a crack.
