@inproceedings{015cd873dd184da3b56fbc7f6ba75645,
title = "Solar Filament Detection using Mask R-CNN",
abstract = "Many algorithms have been proposed to detect solar filaments from H-Alpha images automatically, most of them use intensity thresholding method which requires intensity normalization. We propose a fast, automatic, and efficient method to detect filaments on H-Alpha images without carrying out intensity normalization using Mask R-CNN. The proposed method was applied on H-Alpha images obtained from Indonesian National Institute of Aeronautics and Space (LAPAN), resulting 96% precision. The average detection speed is 0.42 seconds per image. The method can be extended to perform detection on other solar features, such as sunspots, flares, and prominences.",
keywords = "automatic detection, filament detection, solar filament, solar image",
author = "Salasa, {Rian Pramudia} and Arymurthy, {Aniati Murni}",
year = "2019",
month = oct,
doi = "10.1109/IWBIS.2019.8935810",
language = "English",
series = "2019 International Workshop on Big Data and Information Security, IWBIS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "67--71",
booktitle = "2019 International Workshop on Big Data and Information Security, IWBIS 2019",
address = "United States",
note = "2019 International Workshop on Big Data and Information Security, IWBIS 2019 ; Conference date: 11-10-2019",
}