Evaluating Faster-RCNN and YOLOv3 for Target Detection in Multi-sensor Data

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Ulhaq, Anwaar ORCID: 0000-0002-5145-7276, Khan, Asim and Robinson, Randall ORCID: 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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