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

2026-04-28

AI Summary

This work addresses the performance degradation in underwater object detection caused by domain shift between training and deployment data. While existing approaches rely on synthetic style transfer that fails to capture real-world complexity, this study proposes a multidimensional underwater domain definition and annotation framework grounded in measurable image quality, illumination, visibility, and acquisition parameters. For the first time, physically meaningful scene and acquisition factors are incorporated into domain modeling, overcoming the limitations of conventional synthetic domain-shift simulations. The framework enables semantically consistent image grouping, fine-grained failure mode analysis, and domain-specific detector evaluation. Systematic validation on public datasets quantifies the impact of individual domain factors on model performance and uncovers previously obscured failure patterns.

Abstract

Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks testing performance for underwater domain shift simulate variability through synthetic style transfer. This fails to capture intrinsic scene factors such as visibility, illumination, scene composition, or acquisition factors, limiting analysis of real-world effects. We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics. Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis. We validate this on public datasets, showing systematic variations across domain factors and revealing hidden failure modes.

Problem

• domain shift
• underwater object detection
• scene factors
• acquisition conditions
• benchmark limitations

Innovation

• domain shift
• underwater object detection
• domain definition
• failure analysis
• scene characteristics