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DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines

  • Missouri University of Science and Technology
  • Washington State University Tri-Cities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Conference on Big Data, BigData 2024
EditorsWei Ding, Chang-Tien Lu, Fusheng Wang, Liping Di, Kesheng Wu, Jun Huan, Raghu Nambiar, Jundong Li, Filip Ilievski, Ricardo Baeza-Yates, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8262-8271
Number of pages10
ISBN (Electronic)9798350362480
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Big Data, BigData 2024 - Washington, United States
Duration: Dec 15 2024 → Dec 18 2024

Publication series

NameProceedings - 2024 IEEE International Conference on Big Data, BigData 2024

Conference

Conference2024 IEEE International Conference on Big Data, BigData 2024
Country/TerritoryUnited States
CityWashington
Period12/15/24 → 12/18/24

ASJC Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation

Keywords

  • automatic annotation
  • Disaster detection
  • image enhancement
  • instance segmentation
  • underground mining

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