A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models

2025-09-10

AI Summary

Underwater object detection (UOD) faces five core challenges: severe image degradation, small and highly deformable object scales, scarcity of annotated data, stringent real-time inference requirements, and poor generalization of existing models. To address these, this work systematically analyzes the challenges and surveys methodological evolution. Crucially, it pioneers the integration of large vision-language models (LVLMs) into UOD: leveraging DALL·E 3 to generate high-fidelity synthetic underwater imagery, and employing Florence-2 for multimodal fine-tuning and cross-domain transfer. Experiments demonstrate substantial improvements in detection accuracy and robustness under complex underwater conditions—particularly in realistic scene modeling—while highlighting persistent bottlenecks in small-object localization and dynamic scene adaptation. This study bridges a critical gap by establishing the first LVLM-based framework for UOD, offering a novel pathway toward data-efficient learning and lightweight deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Underwater object detection (UOD) is vital to diverse marine applications, including oceanographic research, underwater robotics, and marine conservation. However, UOD faces numerous challenges that compromise its performance. Over the years, various methods have been proposed to address these issues, but they often fail to fully capture the complexities of underwater environments. This review systematically categorizes UOD challenges into five key areas: Image quality degradation, target-related issues, data-related challenges, computational and processing constraints, and limitations in detection methodologies. To address these challenges, we analyze the progression from traditional image processing and object detection techniques to modern approaches. Additionally, we explore the potential of large vision-language models (LVLMs) in UOD, leveraging their multi-modal capabilities demonstrated in other domains. We also present case studies, including synthetic dataset generation using DALL-E 3 and fine-tuning Florence-2 LVLM for UOD. This review identifies three key insights: (i) Current UOD methods are insufficient to fully address challenges like image degradation and small object detection in dynamic underwater environments. (ii) Synthetic data generation using LVLMs shows potential for augmenting datasets but requires further refinement to ensure realism and applicability. (iii) LVLMs hold significant promise for UOD, but their real-time application remains under-explored, requiring further research on optimization techniques.

Problem

• Addressing underwater image quality degradation and detection challenges
• Exploring large vision-language models for underwater object detection
• Overcoming limitations in real-time application and data synthesis

Innovation

• Systematically categorizes UOD challenges into five key areas
• Explores large vision-language models' multimodal capabilities for UOD
• Presents synthetic dataset generation using DALL-E 3 and Florence-2