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用于铁路分割和障碍物检测的无人机捕获数据集。

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

作者信息

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

机构信息

School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.

Department of Self-Development Skill, Common First Year Deanship, King Saud University, 11362, Riyadh, Saudi Arabia.

出版信息

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

DOI:10.1038/s41597-024-03952-3
PMID:39622935
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11612275/
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.

摘要

安全在铁路行业至关重要，因为铁路每天运送数百万乘客和员工，因此预防伤亡至关重要。为了确保乘客安全，在不久的将来，计算机视觉、无人机（UAV）和人工智能将成为日常评估铁路环境的重要工具。一个用于铁路分割和障碍物检测的无人机捕获数据集（UAV-RSOD）包含无人机在铁路场景中各种障碍物上捕获的高分辨率图像，可实现铁路自动提取和障碍物检测。该数据集包括315张原始图像，以及630张用于铁路语义分割的标注图像和630张掩码图像。该数据集由无人机捕获的315张原始图像组成，用于目标检测和障碍物检测。为了增加用于训练目的的数据集多样性，我们应用了数据增强技术，将数据集扩展到2002张用于障碍物检测的增强和标注图像，涵盖铁路线上六种不同类型的障碍物。此外，我们提供原始的315张图像以及增强脚本，允许用户根据需要生成自己的增强数据，提供了一个更具可持续性和可定制性的选项。数据集中的每张图像都用边界框准确标注，并分为六类，包括人、巨石、桶、树枝、杰瑞罐和铁棒。这种全面的分类和详细的标注使该数据集成为从事铁路领域计算机视觉应用研究的人员和开发人员的重要工具。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/a4daad8476d6/41597_2024_3952_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/486b98a27a13/41597_2024_3952_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/e06373183a51/41597_2024_3952_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/f75db3ba81df/41597_2024_3952_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/5701a084f2cf/41597_2024_3952_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/772132232dc6/41597_2024_3952_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db7a/11612275/78d039ea27ec/41597_2024_3952_Fig13_HTML.jpg

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