An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection

Rampriya R S; Taher Al-Shehari; Sabari Nathan; Jenefa A; Suganya R; Shunmuga Perumal P; Taha Alfakih; Hussain Alsalman

doi:10.1038/s41597-024-03952-3

An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection

Sci Data. 2024 Dec 2;11(1):1315. doi: 10.1038/s41597-024-03952-3.

Authors

Rampriya R S¹, Taher Al-Shehari², Sabari Nathan³, Jenefa A⁴, Suganya R⁵, Shunmuga Perumal P⁶, Taha Alfakih⁷, Hussain Alsalman⁸

Affiliations

¹ School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India. [email protected].
² Department of Self-Development Skill, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia.
³ Couger Inc, Tokyo, 150-0001, Japan.
⁴ School of CST, Karunya Institute of Technology and Sciences, Coimbatore, 641114, Tamilnadu, India.
⁵ School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamilnadu, India.
⁶ School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
⁷ Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.
⁸ Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.

Abstract

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain.