Deep gabor neural network for automatic detection of mine-like objects in sonar imagery

Hoang Thanh Le*, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized Gabor layer which improves the generalization capability and computational efficiency. The steerable Gabor filtering modules are embedded within the cascaded layers to enhance the scale and orientation decomposition of images. The entire deep Gabor neural network is trained in an end-to-end manner from input sonar images with annotated mine-like objects. An extensive experimental evaluation on a real sonar dataset shows that the proposed method achieves competitive performance compared to the existing approaches.

Original languageEnglish
Article number9095329
Pages (from-to)94126-94139
Number of pages14
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Gabor layer
  • Gabor neural network detector
  • mine-like objects
  • side-scan sonar

Fingerprint

Dive into the research topics of 'Deep gabor neural network for automatic detection of mine-like objects in sonar imagery'. Together they form a unique fingerprint.

Cite this