Speakers
Description
The Baltic Sea contains an estimated 300,000 tons of unexploded ordnance (UXO), presenting ongoing environmental and safety challenges.
While multibeam echosounder (MBES) surveys are the standard for detecting these hazards due to their high positional accuracy, the acoustic signatures of munitions often resemble geological features or debris, necessitating manual visual verification.
We present an autonomous three-stage framework designed to automate this process.
The system integrates on-device MBES processing with a GPU-accelerated geomorphometric classification dictionary and a Vision Transformer (ViT) priority classifier, both trained on expert-annotated data.
This pipeline picks and prioritizes potential targets and automatically generates survey routes optimized for current environmental conditions for a camera-equipped autonomous underwater vehicle (AUV), which then autonomously performs a photogrammetric survey of each target.
Field testing in the Baltic Sea demonstrates that this system is capable of identifying and revisiting relevant potential targets, as verified by human experts. These results suggest that linking automated acoustic detection with targeted optical mapping can improve the efficiency of large-scale maritime clearance operations and reduce the need for human intervention in the survey process.