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Moon Detection Dataset for YOLOv8
This dataset was developed as part of the CubeRT-02 project, a CubeSat mission aimed at testing AI-powered vision systems in aerospace contexts. It consists of 7500+ annotated images for object detection of the Moon, optimized for use with the YOLOv8 architecture.
📸 Dataset Collection & Annotation
Initial set: ~400 web-sourced images used for theoretical model exploration.
Main dataset: 7500+ images captured over 6 months using smartphones and amateur camera devices, reflecting real-world scales, perspectives, and conditions.
Cleaning: Manual filtering of blurry, low-quality, or irrelevant images.
Annotation: Performed via Roboflow platform with bounding boxes for YOLOv8.
Augmentation: Basic augmentations applied during preparation; none used during final training due to negative effects on performance.
📁 Dataset Structure
This dataset follows the YOLOv8 format. A Python script and a YAML configuration file are included to help you easily train or test the dataset using Ultralytics' YOLOv8 implementation.
Authors
Luis Adrian Cabrera | https://github.com/LuisAdrian5519
Jesse Banda Chaidez | https://github.com/Jessebnda
Contributors
José Alejandro Padilla Pérez | https://github.com/PadillaPepe777
Erick Blanco Nakashima
Juan Pablo Riojas Pesqueira
Guillermo Villegas
Juan Adrian Astorga
Julio Castañeda
Juan Pablo Aboytes
Maximo Millán Cabrera
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