RDD2020: An Image Dataset for Smartphone-based Road Damage Detection and Classification
The RDD2020 dataset contains 26336 road images collected from India, Japan, and the Czech Republic with more than 31000 instances of road damage. The dataset contains annotation for four damage categories: Longitudinal Cracks(D00), Transverse Cracks(D10), Alligator Cracks(D20), and Potholes(D40); and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images in RDD2020 were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions. Further, the machine learning researchers can use the datasets for benchmarking the performance of different algorithms for solving other problems of the same type (classification, object detection, etc.). For instance, the Global Road Damage Detection Challenge (GRDDC'2020), organized as an IEEE Big Data Cup in 2020, utilized the dataset RDD2020 to evaluate the road damage detection models proposed by the participants. An overview of the challenge is provided in the video https://www.youtube.com/watch?v=8sh70wjn1aI. The readers may access the latest updates and the articles related to the dataset at https://www.researchgate.net/project/Global-Road-Damage-Detection.
Steps to reproduce
Kindly refer to the following articles: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132, 103935. 10.1016/j.autcon.2021.103935. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2021). RDD2020: An annotated image dataset for automatic road damage detection using deep learning. Data in brief, 36, 107133. 10.1016/j.dib.2021.107133. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., & Sekimoto, Y. (2020). Global Road Damage Detection: State-of-the-art Solutions. IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5533-5539, doi: 10.1109/BigData50022.2020.9377790. Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., & Omata, H. (2021). Generative adversarial network for road damage detection. Computer‐Aided Civil and Infrastructure Engineering, 36(1), 47-60. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1127-1141.