A Deep Neural Network for Counting Vessels in Sonar Signals

Hamed Habibi Aghdam*, Robert Laganière, Emil Petriu, Martin Bouchard, Philip Wort

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings
EditorsCyril Goutte, Xiaodan Zhu
Number of pages13
Publication statusPublished - 6 May 2020
Event33rd Canadian Conference on Artificial Intelligence: Canadian AI 2020 - Virtuell, Canada
Duration: 13 May 202015 May 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference33rd Canadian Conference on Artificial Intelligence


  • Deep neural networks
  • Sonar signal
  • Vessel counting

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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