Open Access

ARTICLE

ProNet: Underwater Forward-Looking Sonar Images Target Detection Network Based on Progressive Sensitivity Capture

Kaiqiao Wang^1,2, Peng Liu^1,2,*, Chun Zhang^1,2

1 Hainan Acoustics Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Haikou, 570105, China
2 Marine Information, Hainan Observation and Research Station, Lingshui, 572423, China

* Corresponding Author: Peng Liu. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2025, 82(3), 4931-4948. https://doi.org/10.32604/cmc.2025.060547

Received 04 November 2024; Accepted 25 December 2024; Issue published 06 March 2025

Abstract

Underwater target detection in forward-looking sonar (FLS) images is a challenging but promising endeavor. The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments. Considering the problems of low imaging resolution, complex background environment, and large changes in target imaging of underwater sonar images, this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture, named ProNet. It progressively captures the sensitive regions in the current image where potential effective targets may exist. Guided by this basic idea, the primary technical innovation of this paper is the introduction of a foundational module structure for constructing a sonar target detection backbone network. This structure employs a multi-subspace mixed convolution module that initially maps sonar images into different subspaces and extracts local contextual features using varying convolutional receptive fields within these heterogeneous subspaces. Subsequently, a Scale-aware aggregation module effectively aggregates the heterogeneous features extracted from different subspaces. Finally, the multi-scale attention structure further enhances the relational perception of the aggregated features. We evaluated ProNet on three FLS datasets of varying scenes, and experimental results indicate that ProNet outperforms the current state-of-the-art sonar image and general target detectors.

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Keywords

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