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- W3023373323 abstract "Monitoring the oceanographic activity of ships in restricted areas is an important task that can be done using sonar signals. To this end, a human expert may regularly analyze passive sonar signals to count the number of vessels in the region. To automate this process, we propose a deep neural network for counting the number of vessels using sonar signals. Our model is different from common approaches for acoustic signal processing in the sense that it has a rectangular receptive field and utilizes temporal feature integration to perform this task. Moreover, we create a dataset including 117K samples where each sample resembles a scenario with at most 3 vessels. Our results show that the proposed network outperforms traditional methods substantially and classifies (96%) of test samples correctly. Also, we extensively analyze the behavior of our network through various experiments. Our codes and the database are available at https://gitlab.com/haghdam/deep_vessel_counting." @default.
- W3023373323 created "2020-05-13" @default.
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- W3023373323 date "2020-01-01" @default.
- W3023373323 modified "2023-09-24" @default.
- W3023373323 title "A Deep Neural Network for Counting Vessels in Sonar Signals" @default.
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- W3023373323 doi "https://doi.org/10.1007/978-3-030-47358-7_25" @default.
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