Dataset Card for RDD_2020

The RDD2020 dataset is a comprehensive collection of 26,336 road images from India, Japan, and the Czech Republic, annotated with over 31,000 instances of road damages. This dataset is designed to support the development and evaluation of machine learning models for automatic road damage detection, offering a valuable resource for municipalities and road agencies for efficient road condition monitoring.

Dataset Details

Dataset Sources [optional]

• Homepage https://data.mendeley.com/datasets/5ty2wb6gvg/1
• Data article: https://doi.org/10.1016/j.dib.2021.107133

Uses

Direct Use

RDD2020 dataset can be directly used for developing and benchmarking machine learning models aimed at automatic detection and classification of road damages. This includes developing new deep learning architectures or modifying existing ones to improve detection accuracy across different types of road damages

Dataset Structure

Data Instance

The data will follow the structure below:

{
  "image_id": "Czech_000248",
  "country": "Czech",
  "type": "train",
  "image": "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x600>",
  "image_path": "train/Czech/images/Czech_000248.jpg",
  "crack_type": ["D20", "D20"],
  "crack_coordinates": {
    "x_min": [188, 3],
    "x_max": [309, 171],
    "y_min": [463, 438],
    "y_max": [509, 519]
  }
}

Data Fields

• "image_id"[string]: ID of the image, created by combining the country plus a sequential number.
• "country"[string]: The country where the photo was taken.
• "type"[string]: The dataset category the image belongs to, such as 'train', 'test1', or 'test2'. "image"[integer]: The image data converted into PIL format.
• "crack_type"[string]: Types of cracks detected in the image.
• "crack_coordinates"[integer]: Contains crack coordinates as integers.

Dataset Creation

Curation Rationale

The RDD2020 dataset was curated with the objective of facilitating the development, testing, and benchmarking of machine learning models for road damage detection, catering specifically to the needs of municipalities and road agencies. A significant aspect of the dataset's curation process was the conversion of images into the Python Imaging Library (PIL) format and the meticulous parsing of XML annotations to ensure a seamless integration between the image data and the associated labels. This conversion process was driven by the need to simplify the handling of image data for machine learning applications, as the PIL format is widely supported by data processing and model training frameworks commonly used in the field.

Additionally, the parsing of XML files to extract detailed annotations about the type and coordinates of road damages allows for precise labeling of the data. This approach ensures that each image is directly associated with its corresponding damage type and location. The dataset's diversity, with images sourced from three different countries, aims to enable the creation of robust models that are effective across various environmental conditions and road infrastructures, thereby broadening the applicability and relevance of the trained models.

Data Collection and Processing

Road images (.jpg) were collected using a vehicle-mounted smartphone, moving at an average speed of about 40Km/h. XML files were created using the LabelImg tool to annotate the road damages present in the images.

Who are the source data producers?

Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Hiroshi Omata, Takehiro Kashiyama, Toshikazu Seto, Alexander Mraz, Yoshihide Sekimot

Annotations

Annotation process

Each image in the dataset comes with corresponding XML files containing annotations in PASCAL VOC format. These annotations describe the location and type of road damages present in the images, categorized into four main types: Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), and Potholes (D40).

Social Impact

The structuring of the RDD2020 dataset into a more accessible and usable format is aimed at having a focused and measurable impact on the management of road infrastructure. The transformation of raw images and XML annotations into a coherent dataset with clearly defined attributes such as photo_id, country, type, pics_array, image_resolution, crack_type, and crack_coordinates creates a powerful tool for municipalities and road agencies. With this structured dataset, these entities can deploy machine learning models to accurately identify and classify road damages like cracks and potholes, which are critical for the maintenance and safety of roadways. In conclusion, the transformation of this raw data into a structured and accessible format not only catalyzes the progress of automated road damage assessment but also potentially engages the public sector in adopting AI-driven solutions for public safety and infrastructure management.

Bias, Risks, and Limitations

The dataset primarily includes images from three countries (India, Japan, and the Czech Republic), which may not fully represent road conditions worldwide. Users should be cautious when generalizing models trained on this dataset to other regions.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.