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RDD2020: An annotated image dataset for automatic road damage detection using deep learning.

Deeksha Arya1,2, Hiroya Maeda2, Sanjay Kumar Ghosh1,3

  • 1Center for Transportation Systems, Indian Institute of Technology Roorkee, 247667, India.

Data in Brief
|June 7, 2021
PubMed
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This summary is machine-generated.

The RDD2020 dataset offers 26,336 road images for developing AI to detect road damage like cracks and potholes. This resource aids in low-cost pavement monitoring and algorithm benchmarking.

Area of Science:

  • Computer Science
  • Civil Engineering
  • Transportation Science

Background:

  • Road damage detection is crucial for infrastructure maintenance and safety.
  • Existing methods for road condition monitoring can be costly and labor-intensive.
  • Automated detection using deep learning offers a scalable solution.

Purpose of the Study:

  • To introduce the RDD2020 dataset for road damage detection research.
  • To provide a comprehensive dataset for training and evaluating deep learning models.
  • To facilitate low-cost road surface condition monitoring.

Main Methods:

  • The RDD2020 dataset comprises 26,336 road images from India, Japan, and the Czech Republic.
  • Images feature over 31,000 annotated instances of four damage types: longitudinal cracks, transverse cracks, alligator cracks, and potholes.
Keywords:
Automatic road condition monitoringCrack recognitionData qualificationDeep learningImagePavement surface condition assessmentQuantificationRoad damage datasetRoad infrastructureSmartphone-based road damage detection and classificationStructural health monitoring

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  • Data was collected using vehicle-mounted smartphones, simulating real-world conditions.

    • Main Results:

    • The dataset enables the development of automated road damage detection and classification systems.
    • It supports research in image classification and object detection tasks related to pavement analysis.
    • Provides a benchmark for evaluating the performance of various machine learning algorithms.

    Conclusions:

    • RDD2020 is a valuable, freely accessible resource for advancing road damage detection technology.
    • The dataset supports the creation of cost-effective road monitoring solutions for municipalities and road agencies.
    • Facilitates further research and development in intelligent transportation systems and infrastructure management.