@inproceedings{e73a07ba5f9c4c8b875132cf14e5e739,
title = "A Deep Neural Network for Counting Vessels in Sonar Signals",
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",
keywords = "Deep neural networks, Sonar signal, Vessel counting",
author = "{Habibi Aghdam}, Hamed and Robert Lagani{\`e}re and Emil Petriu and Martin Bouchard and Philip Wort",
year = "2020",
month = may,
day = "6",
doi = "10.1007/978-3-030-47358-7_25",
language = "English",
isbn = "9783030473570",
series = "Lecture Notes in Computer Science",
pages = "257--269",
editor = "Cyril Goutte and Xiaodan Zhu",
booktitle = "Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings",
note = "33rd Canadian Conference on Artificial Intelligence : Canadian AI 2020 ; Conference date: 13-05-2020 Through 15-05-2020",
}