1 code implementation • IEEE Access 2022 • Lucas Cesar Ferreira Domingos, Paulo E. Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth, Karl Sammut
However, high accuracies of 94. 95% were achieved using CQT as the preprocessing filter for a ResNet-based convolutional neural network, providing a trade-off between model complexity and accuracy; a result that is more than 10% higher than previously reported approaches.
no code implementations • Sensors 2022 • Lucas Cesar Ferreira Domingos, Paulo E Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth, Karl Sammut
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships).
no code implementations • 12 Sep 2017 • Victor Stamatescu, Peter Barsznica, Manjung Kim, Kin K. Liu, Mark McKenzie, Will Meakin, Gwilyn Saunders, Sebastien C. Wong, Russell S. A. Brinkworth
We present a novel data set made up of omnidirectional video of multiple objects whose centroid positions are annotated automatically.