Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 24 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

An underwater object detection method based on generative degradation simulation and hard-sample learning

  • Feng Wang
  • Baorong Zhan
  • Shaojiang Liu
  • Fang Deng
  • Lixia Li
Open at sourcePDFDOICite

Related

  1. York-Tank: A Degradation-Aware and Difficulty-Stratified Stereo Dataset for Underwater Visual SLAM
    Sensors (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  2. Generative marginal learning for finite-size molecular simulation with application to stress calculation
    Cheng Yuan
    arXivPreprintCC BY-NC-ND 4.0Full textComputational physicsStatistical & nonlinear physicsarXiv
  3. Fast BIB simulation at a future Muon Collider with generative machine learning
    Radha Mastandrea, Shiyu Peng, Benjamin Rosser +1
    arXivPreprintCC BY 4.0Full textApplied physics & instrumentationHigh-energy & particle physicsarXiv
  4. Parnassus for the CLD Detector: A Generative Machine-Learning Surrogate for Detector Simulation and Reconstruction at the FCC-ee
    Umar Sohail Qureshi, Benjamin Nachman, Caterina Vernieri
    arXivPreprintCC BY 4.0Full textApplied physics & instrumentationComputational physicsarXiv
  5. Cross-Modal Semantic Successive Refinement for Generative Image Communication over Underwater Acoustic Channels
    Xu Pei, Bin Wang, Guangxu Zhu +2
    arXivPreprintCC BY 4.0Full textGeneral mathematicsarXiv

Cite

BibTeXRIS
BibTeX
@article{wang2026underwater,
  title = {{An underwater object detection method based on generative degradation simulation and hard-sample learning}},
  author = {Feng Wang and Baorong Zhan and Shaojiang Liu and Fang Deng and Lixia Li},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-76673-9},
  url = {https://www.nature.com/articles/s41598-026-76673-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - An underwater object detection method based on generative degradation simulation and hard-sample learning
AU  - Feng Wang
AU  - Baorong Zhan
AU  - Shaojiang Liu
AU  - Fang Deng
AU  - Lixia Li
PY  - 2026
DA  - 2026/10/18
JO  - Scientific Reports
DO  - 10.1038/s41598-026-76673-9
UR  - https://www.nature.com/articles/s41598-026-76673-9
SN  - 2045-2322
ER  -