Evaluating Faster-RCNN and YOLOv3 for Target Detection in Multi-sensor Data
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Ulhaq, Anwaar 
ORCID: https://orcid.org/0000-0002-5145-7276, Khan, Asim and Robinson, Randall ORCID: https://orcid.org/0000-0001-8425-0709
(2020)
Evaluating Faster-RCNN and YOLOv3 for Target Detection in Multi-sensor Data.
In: 2nd Applied Statistics and Policy Analysis Conference: ASPAC2019, 5 Sep 2019 - 6 Sep 2019, Wagga Wagga, Australia.
| Item type | Conference or Workshop Item (Paper) |
| URI | https://vuir.vu.edu.au/id/eprint/41987 |
| Official URL | https://link.springer.com/chapter/10.1007/978-981-... |
| ISBN | 9789811517341 (print) 9789811517358 (online) |
| Subjects | Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Current > Division/Research > College of Science and Engineering Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
| Keywords | deep object detectors; You Look Once Only; Faster Region based Convolutional Neural Networks; images; multi-sensor data; night |
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