Deep learning for crescent detection and recognition: Implementation of Mask R-CNN to the observational Lunar dataset collected with the Robotic Lunar Telescope System-Reference-Cited by-同舟云学术

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Deep learning for crescent detection and recognition: Implementation of Mask R-CNN to the observational Lunar dataset collected with the Robotic Lunar Telescope System

Published:2023-10 Issue: Volume:45 Page:100757
ISSN:2213-1337
Container-title:Astronomy and Computing
language:en
Short-container-title:Astronomy and Computing

Author:

Muztaba R.^ORCID,Malasan H.L.,Djamal M.

Funder

Kementerian Riset dan Teknologi Republik Indonesia

Bundesministerium für Forschung und Technologie

Publisher

Elsevier BV

Subject

Computer Science Applications,Astronomy and Astrophysics,Space and Planetary Science

Reference28 articles.

1. Efficient interactive annotation of segmentation datasets with polygon-rnn++;Acuna,2018

2. Anon,, 0000, Manta User Guide (website), https://www.alliedvision.com/en/products/camera-series/manta/.

3. The first visibility of lunar crescent;Bruin;Vistas Astron.,1997

4. Human contrast threshold and astronomical visibility;Crumey;Mon. Not. R. Astron. Soc.,2014

5. Stray light analysis of moon-based visible and near-infrared system;Deng,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning;Frontiers in Neurorobotics;2024-01-04

2. HilalPy: Software to analyse lunar sighting criteria;Software Impacts;2023-11

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