Automatic Object Classification for Low-Frequency Active Sonar using Convolutional Neural Networks
| Autor: | Pietro Stinco, Alessandra Tesei, Gabriele Ferri, Jessica M. Topple, Kevin Le Page, Jeffrey R. Bates, Giovanni De Magistris, Gaetano Canepa |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
010505 oceanography business.industry Computer science Sea trial 020206 networking & telecommunications Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Clutter Artificial intelligence Marine mammals and sonar Underwater business Classifier (UML) 0105 earth and related environmental sciences |
| Zdroj: | OCEANS 2019 MTS/IEEE SEATTLE. |
| DOI: | 10.23919/oceans40490.2019.8962860 |
| Popis: | Neural Networks are proposed to classify underwater objects from active sonar system data collected for underwater surveillance. The raw signal is processed, transformed in the time-frequency domain and classified (object of interest/clutter). The values of the neural network parameters (weights and biases) are learned using data collected during two sea trials with an Echo-Repeater as an object of interest. The classifier is then validated using data from a third sea trial in different geographical locations and environmental conditions. In our validation dataset, the CNN classifier significantly reduces the number of false alarms and outperform traditional feature-based classifier that we previously developed. |
| Databáze: | OpenAIRE |
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