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

• URL: http://arxiv.org/abs/2604.26174v1
• Date: Tue, 28 Apr 2026 23:28:14 GMT
• Title: Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection
• Abstract summary: We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics.<n>Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis.
• Score: 9.468095570700642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality [9.468095570700642]
  We introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels.<n>We perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance.<n>We highlight how this can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.
  arXiv  Detail & Related papers  (2026-07-12T05:11:59Z)
• Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection [46.724153214302184]
  Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments.<n>This study evaluates the transferability of three widely used flow-based feature sets (Argus, Zeek and CICFlowMeter) across four widely used datasets representing heterogeneous IoT and Industrial IoT network conditions.
  arXiv  Detail & Related papers  (2026-02-27T10:16:28Z)
• Harmonizing the Deep: A Unified Information Pipeline for Robust Marine Biodiversity Assessment Across Heterogeneous Domains [0.769971486557519]
  This work establishes the foundational detection layer for a multi-year invasive species monitoring initiative targeting Arctic and Atlantic marine ecosystems.<n>We develop a Unified Information Pipeline that standardises heterogeneous datasets into a comparable information flow.<n>We find that structural factors, such as scene composition, object density, and contextual redundancy, explain cross-domain performance loss.
  arXiv  Detail & Related papers  (2026-01-20T13:51:55Z)
• Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
  Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc.<n>It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation.<n>We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
  arXiv  Detail & Related papers  (2024-12-29T17:59:45Z)
• Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper [0.6437284704257459]
  Domain adaptation aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions.<n>This review delves into advanced methodologies for domain adaptation, including adversarial learning, discrepancy-based, multi-domain, teacher-student, ensemble, and Vision Language Models.<n>Special attention is given to strategies that minimize the reliance on extensive labeled data, particularly in scenarios involving synthetic-to-real domain shifts.
  arXiv  Detail & Related papers  (2024-12-23T06:34:23Z)
• PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment [59.9103803198087]
  We propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA)<n>By leveraging underwater radiative transfer theory, we integrate physics-based imaging estimations to establish quantitative metrics for these distortions.<n>The proposed model accurately predicts image quality scores and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-20T03:31:45Z)
• FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation [65.01601309903971]
  We introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs) Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-25T03:54:01Z)
• Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection [18.39732229138302]
  This paper introduces the concept of Domain Similarity-Perceived Label Assignment (DSP) Through domain-specific data augmentation techniques, we achieved state-of-the-art results on the underwater cross-domain object detection benchmark S-UODAC 2020.
  arXiv  Detail & Related papers  (2023-12-20T05:45:56Z)
• ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments [1.2624532490634643]
  This research presents ADOD, a novel approach to address domain generalization in underwater object detection. Our method enhances the model's ability to generalize across diverse and unseen domains, ensuring robustness in various underwater environments.
  arXiv  Detail & Related papers  (2023-12-11T19:20:56Z)
• Domain Adaptation for Underwater Image Enhancement [51.71570701102219]
  We propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to minimize the inter-domain and intra-domain gap. In the first phase, a new dual-alignment network is designed, including a translation part for enhancing realism of input images, followed by an enhancement part. In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded.
  arXiv  Detail & Related papers  (2021-08-22T06:38:19Z)
• Phase Consistent Ecological Domain Adaptation [76.75730500201536]
  We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
  arXiv  Detail & Related papers  (2020-04-10T06:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.