Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

A team from Queensland University of Technology (QUT) has introduced a novel labeling framework to tackle domain shift in underwater object detection, presented as a poster at the ICRA 2026 Workshop S2S. Domain shift—where differences between training and deployment data distributions degrade model performance—is especially problematic in underwater environments due to factors like visibility, illumination, scene composition, and acquisition methods. Existing benchmarks simulate domain variability through synthetic style transfer, which fails to capture these intrinsic scene factors.

Wille et al.'s framework defines underwater domains using measurable characteristics, enabling semantically consistent image grouping and domain-specific evaluation of detection performance, including failure analysis. Validated on public datasets, the approach reveals systematic variations across domain factors and exposes hidden failure modes that prior benchmarks missed. This work bridges the gap between synthetic simulations and real-world underwater conditions, providing a more robust tool for developing AI systems for marine robotics and computer vision applications.