README.md

---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---

# 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 Description

- **Source:** [Mendeley Data](https://data.mendeley.com/datasets/5ty2wb6gvg/1) - DOI: 10.17632/5ty2wb6gvg.1  
- **Size:** 1.13 GB  
- **Format:** Images (JPEG) and Annotations (XML in PASCAL VOC format)  
- **Resolution:**   
  - India: 720 × 720 pixels  
  - Japan and Czech: 600 × 600 pixels  
- **Categories:** Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), Potholes (D40)  

- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **License:** https://creativecommons.org/licenses/by/4.0/

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

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


## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

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

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]
  }
}




## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->
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

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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).


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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