Software

Radio Galaxy Detection - Computer Vision Algorithms

Commonwealth Scientific and Industrial Research Organisation

Gupta, Nikhel ; Hayder, Zeeshan ; Norris, Ray ; Huynh, Minh ; Petersson, Lars

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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://hdl.handle.net/102.100.100/602494?index=1&rft.title=Radio Galaxy Detection - Computer Vision Algorithms&rft.identifier=http://hdl.handle.net/102.100.100/602494?index=1&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This collection of machine-learning algorithms for detecting radio sources and their infrared host galaxies. We introduce Gal-DETR, Gal-Deformable DETR and Gal-DINO multimodal models for object detection. These models are built upon the DETR (Carion et al., 2020), Deformable DETR (Zhu et al., 2021), and DINO (Zhang et al., 2022) algorithms, which are optimal methods for predicting bounding box instances and categories of required objects. We extended their capabilities by incorporating keypoint detection. In addition to the bounding boxes employed to detect extended radio galaxies, the integration of keypoint detection techniques offers a complementary approach for identifying infrared hosts.For more details, see the RadioGalaxyNET paper in the PASA journal and the NeurIPS 2023 conference workshop.&rft.creator=Gupta, Nikhel &rft.creator=Hayder, Zeeshan &rft.creator=Norris, Ray &rft.creator=Huynh, Minh &rft.creator=Petersson, Lars &rft.date=2023&rft.edition=v1&rft_rights=Apache Licence 2.0 https://research.csiro.au/dap/licences/apache-2-0-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=Computer Vision&rft_subject=Object Detection&rft_subject=Machine Learning methods&rft_subject=Radio Galaxies&rft_subject=ASKAP telescope&rft_subject=Machine learning not elsewhere classified&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=Computer Program&rft.language=English Access the software

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Apache Licence 2.0
https://research.csiro.au/dap/licences/apache-2-0-licence/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

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This collection of machine-learning algorithms for detecting radio sources and their infrared host galaxies. We introduce Gal-DETR, Gal-Deformable DETR and Gal-DINO multimodal models for object detection. These models are built upon the DETR (Carion et al., 2020), Deformable DETR (Zhu et al., 2021), and DINO (Zhang et al., 2022) algorithms, which are optimal methods for predicting bounding box instances and categories of required objects. We extended their capabilities by incorporating keypoint detection. In addition to the bounding boxes employed to detect extended radio galaxies, the integration of keypoint detection techniques offers a complementary approach for identifying infrared hosts.

For more details, see the RadioGalaxyNET paper in the PASA journal and the NeurIPS 2023 conference workshop.

Available: 2023-12-01

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Subjects

ASKAP telescope | Computer Vision | Information and Computing Sciences | Machine Learning methods | Machine Learning | Machine Learning Not Elsewhere Classified | Object Detection | Radio Galaxies |

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Identifiers

Handle : 102.100.100/602494
URL : data.csiro.au/collection/csiro:61069