@Article{cmc.2025.060547,
AUTHOR = {Kaiqiao Wang, Peng Liu, Chun Zhang},
TITLE = {ProNet: Underwater Forward-Looking Sonar Images Target Detection Network Based on Progressive Sensitivity Capture},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {3},
PAGES = {4931--4948},
URL = {http://www.techscience.com/cmc/v82n3/59923},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.060547}
}