Researchers propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics to evaluate object detection performance.

A team led by Melanie Wille published a study proposing a labeling framework for underwater object detection that defines domains using measurable image, scene, and acquisition characteristics, rather than relying solely on synthetic style transfer.

The framework captures physically meaningful factors including visibility, illumination, scene composition, and acquisition factors, enabling semantically consistent image grouping and domain-specific evaluation of detection performance with failure analysis.

Validation on public datasets revealed systematic variations across domain factors and hidden failure modes, addressing limitations in existing benchmarks that fail to capture intrinsic scene factors in underwater environments.