Annotated Bridge Inspection Imagery and Videos From AI-Powered Defect Detection in Kansas Bridges [Dataset]
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2025-11-03
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By Cui, Qingbin
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Alternative Title:SMART Counties in Kansas [Dataset]
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Abstract:This dataset contains annotated images and videos generated from drone-collected bridge inspection imagery used in AI-based defect detection research across four Kansas counties. The files include visual outputs from YOLOv11 instance segmentation models trained to identify and outline eight types of concrete surface defects: Crack, ACrack, Efflorescence, WConccor, Spalling, Wetspot, Rust, and ExposedRebars. Each annotated image shows detection polygons with defect labels and per-image class counts. Supplementary TXT files summarize per-image defect counts.
These data were collected as part of research on AI-powered bridge condition assessment conducted at the University of Maryland in collaboration with Kansas counties. The dataset supports reproducibility, benchmarking, and further research on automated infrastructure inspection.
The total size of the ZIP file is 25.4 GB.
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Content Notes:This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation: Tsegaye, N. T. (2025). Annotated bridge inspection imagery and videos from AI-powered defect detection in Kansas bridges (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17477702; Training Code: https://doi.org/10.5281/zenodo.17488232; Deployment Code: https://doi.org/10.5281/zenodo.17488363
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Main Document Checksum:urn:sha-512:feb5d67f80139b6ee53c40b5aebb39544a45babb5aaae884ab63f094c4b9a37393338aa50d1262bc557d65afe07d40a65fbf65ac3223d7f22c86e56b1fe288c7
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Supporting Files
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