Open Access
| Issue |
J. Space Weather Space Clim.
Volume 16, 2026
|
|
|---|---|---|
| Article Number | 16 | |
| Number of page(s) | 15 | |
| DOI | https://doi.org/10.1051/swsc/2026014 | |
| Published online | 07 May 2026 | |
- Afandi N, Sabri N, Umar R, Monstein C. 2020. Burst-Finder: burst recognition for E-CALLISTO spectra. Indian J Phys 94(7): 947–957. https://doi.org/10.1007/s12648-019-01551-2. [Google Scholar]
- Benz AO, Monstein C, Meyer H, Manoharan PK, Ramesh R, et al. 2009. A world-wide net of solar radio spectrometers: e-CALLISTO. Earth Moon Planets 104(1): 277–285. https://doi.org/10.1007/s11038-008-9267-6. [NASA ADS] [CrossRef] [Google Scholar]
- Bochkovskiy A, Wang C-Y, Liao H-YM. 2020. YOLOv4: Optimal speed and accuracy of object detection. PrearXiv https://arxiv.org/abs/2004.10934. [Google Scholar]
- Bussons Gordo J, Fernández Ruiz M, Prieto Mateo M, Alvarado Díaz J, Chávez de la O F, et al. 2023. Automatic burst detection in solar radio spectrograms using deep learning: deARCE Method. Sol Phys 298: 82. https://doi.org/10.1007/s11207-023-02171-0. [Google Scholar]
- Deng J, Yuan G, Zhou H, Wu H, Tan C. 2024. Real-time automated detection of multi-category solar radio bursts. Astrophys Space Sci 369(10): 99. https://doi.org/10.1007/s10509-024-04364-w. [Google Scholar]
- Diwan T, Ani A, Tembhurne J. 2022. Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools Appl 82: 9243–9275. https://doi.org/10.1007/s11042-022-13644-y. [Google Scholar]
- Doherty J, Gardiner B, Kerr E, Siddique N, Manvi SS. 2022. Comparative study of activation functions and their impact on the YOLOv5 object detection odel. In: Pattern recognition and artificial intelligence, El Yacoubi M, Granger E, Yuen PC, Pal U, Vincent N (Eds.), Springer International Publishing, Cham. pp. 40–52. ISBN 978-3-031-09282-4. https://doi.org/10.1007/978-3-031-09282-4_4. [Google Scholar]
- Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A. 2010. The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2): 303–338. https://doi.org/10.1007/s11263-009-0275-4. [Google Scholar]
- He H, Yuan G, Zhou H, Tan C, Guo S. 2023. Solar radio burst detection based on the MobileViT-SSDLite lightweight model. Astrophys J Suppl Ser 269(2): 51. https://doi.org/10.3847/1538-4365/ad036c. [Google Scholar]
- Khanam R, Hussain M. 2024. What is YOLOv5: A deep look into the internal features of the popular object detector. PrearXiv https://arxiv.org/abs/2407.20892. [Google Scholar]
- Lin T-Y, Maire M, Belongie S, Bourdev L, Girshick R, et al. 2015. Microsoft COCO: Common Objects in Context. https://arxiv.org/abs/1405.0312. [Google Scholar]
- Lobzin VV, Cairns IH, Robinson PA, Steward G, Patterson G. 2009. Automatic recognition of type III solar radio bursts: automated radio burst identification system method and first observations. Space Weather 7: 4. https://doi.org/10.1029/2008SW000425. [Google Scholar]
- Lobzin VV, Cairns IH, Robinson PA, Steward G, Patterson G. 2010. Automatic recognition of coronal type II radio bursts: the automated radio burst identification system method and first observations. Astrophys J Lett 710(1): L58. https://ui.adsabs.harvard.edu/link_gateway/2010ApJ...710L..58L/doi:10.1088/2041-8205/710/1/L58. [Google Scholar]
- Monstein C, Csillaghy A, Benz AO. 2023. CALLISTO Quicklook solar spectrogram plots. Accessed: 2025-08-18. https://doi.org/10.48322/WY0B-TQ35. https://spase-metadata.org/ISWI/DisplayData/Callisto/FAS/PT15M. [Google Scholar]
- Pick M, Vilmer N. 2008. Sixty-five years of solar radioastronomy: Flares, coronal mass ejections and Sun-Earth connection. Astron Astrophys Rev 16: 1–153. https://doi.org/10.1007/s00159-008-0013-x. [CrossRef] [Google Scholar]
- Rainio O, Teuho J, Klén R. 2024. Evaluation metrics and statistical tests for machine learning. Sci Rep 14(1): 6086. https://doi.org/10.1038/s41598-024-56706-x. [Google Scholar]
- Redmon J, Divvala S, Girshick R, Farhadi A. 2016. You only look once: Unified, real-time object detection. https://arxiv.org/abs/1506.02640. [Google Scholar]
- Saponara S, Elhanashi A. 2022. Impact of image resizing on deep learning detectors for training time and model performance. In: Applications in electronics pervading industry, environment and society, Saponara S, De Gloria A (Eds.), Springer International Publishing, Cham., ISBN 978-3-030-95498-7. https://doi.org/10.1007/978-3-030-95498-7_2. [Google Scholar]
- Solovyev R, Wang W, Gabruseva T. 2021. Weighted boxes fusion: Ensembling boxes from different object detection models. Image Vision Comput 107: 1–6. https://doi.org/10.48550/arXiv.1910.13302. [Google Scholar]
- Wang M, Yuan G, He H, Tan C, Wu H, Zhou H. 2025. Multi-category solar radio burst detection based on task-aligned one-stage object detection model. Astrophys Space Sci 370(3): 23. https://doi.org/10.48550/arXiv.2503.16483. [Google Scholar]
- White SM. 2024. Solar radio bursts and space weather. https://arxiv.org/abs/2405.00959. [Google Scholar]
- Zhang W, Wang B, Wu Z, Chen Y, Yan F. 2024. Identification and extraction of type II and III radio bursts based on YOLOv7. Astron Astrophys 683: A90. https://doi.org/10.1051/0004-6361/202348026. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.
