Acoustic Camera-Based Adaptive Mosaicking Framework for Underwater Structures Inspection in Complex Marine Environments

Abstract

This study considers the data processing for acoustic cameras and achieves the generation of high-quality acoustic panoramas through image mosaicking. Thanks to high-resolution imaging, acoustic cameras are increasingly popular in ocean engineering. However, their narrow detection field of view makes it challenging to intuitively perceive marine environments. Generating large panoramas through mosaicking is a good way to solve this problem. Due to limitations such as low resolution, low signal-to-noise ratio, weak textures, and nonlinear distortions in acoustic images, most classic mosaicking pipelines do not perform well. This study proposes an adaptive mosaicking framework for acoustic cameras that integrates image denoising, feature matching, and mosaicking modules. It can generate large-area panoramas from overlapping acoustic camera images without any assumptions regarding the scenes. The overall process consists of three main steps: first, introduce a self-supervised denoising strategy to preprocess acoustic images to effectively remove complex noise; second, use a detector-free paradigm to achieve feature matching between adjacent acoustic images. This paradigm matches dense pixels in the high-level structure of images rather than relying on isolated geometric features, addressing the matching challenges in weak-texture areas. Third, design a mosaicking approach based on matching results to generate acoustic panoramas. This framework has been verified experimentally, and the results show that it canrobustly and effectively mosaic acoustic images, providing a novel reference and solution for underwater structures inspection in complex marine environments.