AI-based boulder detection in sonar data – bridging the gap from experimentation to application

Abstract

The detection of boulders in hydroacoustic data is essential for a range of environmental, economic and marine planning applications. The manual interpretation of hydroacoustic data for object detection is a non-trivial, tedious and subjective task. Using the conventional means accessible to hydrographic professionals, it is nearly impossible to locate all boulders or rule out their presence for extended areas of interest. Although it has been shown that AI can do the job quickly and reproducibly, earlier work has not progressed beyond scientific experiments. As a result, AI software have not been routinely integrated into the workflows of institutions involved in hydrographic data acquisition and processing, or oceanographic analysis. This paper presents a workflow for fully automated boulder detection in hydroacoustic data. A graphical user interface enables training and evaluation of detection models, boulder detection model execution, and post-processing of detection results without programming. The workflow is demonstrated on data from the southern Baltic Sea. Validation results of the detection for various data inputs include a mAP-50 of 77.83 % for raster images of backscatter intensities based on side-scan sonar, a mAP-50 of 70.46 % for raster images of slope angles based on multibeam echosounder and a mAP-50 of 44.02 % for backscatter and bathymetric data given as 3D point clouds.