Curated Dataset and Inference Results for AI-Based Bridge Defect Detection Using Images and Videos [supporting dataset]

Details

• Alternative Title:
  Drone-based AI Bridge Inspection Dataset (Kansas NCRPC SMART Stage 1) [additional dataset]
• Creators:
  Tsegaye, Natnael Taye
• Corporate Creators:
  North Central Regional Planning Commission
• Corporate Publisher:
  North Central Regional Planning Commission
• Contributor:
  Cui, Qingbin ; Zhang, Yunfeng
• Corporate Contributors:
  Strengthening Mobility and Revolutionizing Transportation (SMART) Program
• Subject/TRT Terms:
  • Datasets
  • Bridges
  • Structural health monitoring
  • Concrete
  • Drones
  • Unmanned aircraft systems
  • Artificial intelligence
• DOI:
  https://doi.org/10.5281/zenodo.19268973
• Resource Type:
  Dataset
• Geographical Coverage:
  United States ; Kansas
• Edition:
  v1
• Description:
  This repository contains curated dataset and inference results used in the study:
  
  "AI-powered bridge defect detection and condition assessment using drone-collected visual data."
  
  Contents:
  
  1. curated_dataset_images_annotations: Annotated images with YOLO-format labels for eight defect classes.
  
  2. curated_inference_results_images: Model predictions on test images.
  
  3. curated_dataset_bridge_videos: Raw bridge inspection videos.
  
  Defect classes:
  
  Crack, ACrack, Efflorescence, WConccor, Spalling, Wetspot, Rust, ExposedRebars.
  
  All annotations are manually created and used as ground truth for training and validation.
  
  Inference results are generated using a trained YOLO-based instance segmentation model.
  
  This dataset supports reproducibility and transparency of the proposed methodology.
  
  The total size of the ZIP file is 20.1 GB.
• 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. (2026). Curated Dataset and Inference Results for AI-Based Bridge Defect Detection Using Images and Videos [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.19268973
• Format:
  ZIP
• Funding:
  69A3552341015 SMARTFY22N1P1G24
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93522/dot_93522_DS1.pdf
• File Type:
  [PDF - 395.69 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; SMART Program ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3039471e655e07688a9b816390872a36a981c322082c587d18a97cd77ffcc315968a097454c4195d6348e8996ae115eb33517c2bb2528c8c13b3b126a03f7c66