RailFOD23: A dataset for foreign object detection on railroad transmission lines.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
16 Jan 2024
16 Jan 2024
Historique:
received:
25
09
2023
accepted:
04
01
2024
medline:
17
1
2024
pubmed:
17
1
2024
entrez:
16
1
2024
Statut:
epublish
Résumé
Artificial intelligence models play a crucial role in monitoring and maintaining railroad infrastructure by analyzing image data of foreign objects on power transmission lines. However, the availability of publicly accessible datasets for railroad foreign objects is limited, and the rarity of anomalies in railroad image data, combined with restricted data sharing, poses challenges for training effective foreign object detection models. In this paper, the aim is to present a new dataset of foreign objects on railroad transmission lines, and evaluating the overall performance of mainstream detection models in this context. Taking a unique approach and leveraging large-scale models such as ChatGPT (Chat Generative Pre-trained Transformer) and text-to-image generation models, we synthesize a series of foreign object data. The dataset includes 14,615 images with 40,541 annotated objects, covering four common foreign objects on railroad power transmission lines. Through empirical research on this dataset, we validate the performance of various baseline models in foreign object detection, providing valuable insights for the monitoring and maintenance of railroad facilities.
Identifiants
pubmed: 38228610
doi: 10.1038/s41597-024-02918-9
pii: 10.1038/s41597-024-02918-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
72Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 62063009
Informations de copyright
© 2024. The Author(s).
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