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| Paper: |
AstroCV - A computer Vision Library for Astronomy |
| Volume: |
522, Astronomical Data Analysis Software and Systems XXVII |
| Page: |
425 |
| Authors: |
González, R. E.; Muñoz, R. P. |
| Abstract: |
We present AstroCV, a computer vision library for
processing and analyzing big astronomical datasets.
The goal of AstroCV is to provide a community repository of high performance Python and C++ algorithms used in the areas of image processing and computer vision.
The current AstroCV library includes methods for the tasks of object recognition, segmentation and classification,
with emphasis in the automatic detection and classification of galaxies.
The underlying models were trained using convolutional neural networks and deep learning techniques, which provide better results than methods based on manual feature engineering and SVMs.
The detection and classification methods were trained end-to-end using public
datasets such as the Sloan Digital Sky Survey (SDSS) and Galaxy Zoo, and private
datasets such as the Next Generation Virgo (NGVS) and Fornax (NGFS) surveys.
The detection and classification methods were trained using the deep learning framework DARKNET and the real-time object detection system YOLO. These methods are implemented in C and CUDA languages and makes intensive use of graphical processing units (GPU). Using a single high-end Nvidia GPU card, it can process a SDSS image in 50 milliseconds and a DECam image in less than 3 seconds.
We provide the open source code, pre-trained networks and python tutorials for using the AstroCV library. |
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