MarineDet: Towards Open-Marine Object Detection

• URL: http://arxiv.org/abs/2310.01931v1
• Date: Tue, 3 Oct 2023 10:13:42 GMT
• Title: MarineDet: Towards Open-Marine Object Detection
• Abstract summary: Open-marine object detection (OMOD for short) is required to detect diverse and unseen marine objects, performing categorization and localization simultaneously. We formulate a joint visual-text semantic space through pre-training and then perform marine-specific training to achieve in-air-to-marine knowledge transfer. experimental results demonstrate the superior performance of MarineDet over existing generalist and specialist object detection algorithms.
• Score: 13.58409623192008
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Marine object detection has gained prominence in marine research, driven by the pressing need to unravel oceanic mysteries and enhance our understanding of invaluable marine ecosystems. There is a profound requirement to efficiently and accurately identify and localize diverse and unseen marine entities within underwater imagery. The open-marine object detection (OMOD for short) is required to detect diverse and unseen marine objects, performing categorization and localization simultaneously. To achieve OMOD, we present \textbf{MarineDet}. We formulate a joint visual-text semantic space through pre-training and then perform marine-specific training to achieve in-air-to-marine knowledge transfer. Considering there is no specific dataset designed for OMOD, we construct a \textbf{MarineDet dataset} consisting of 821 marine-relative object categories to promote and measure OMOD performance. The experimental results demonstrate the superior performance of MarineDet over existing generalist and specialist object detection algorithms. To the best of our knowledge, we are the first to present OMOD, which holds a more valuable and practical setting for marine ecosystem monitoring and management. Our research not only pushes the boundaries of marine understanding but also offers a standard pipeline for OMOD.

Related papers

• OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models [57.19693589926157]
  OceanPile is a large-scale multimodal corpus designed for ocean foundation models.<n>It comprises three key components: OceanCorpus, OceanInstruction, and OceanBenchmark.<n>All datasets are publicly released to advance the field of marine artificial intelligence.
  arXiv  Detail & Related papers  (2026-04-25T14:53:37Z)
• OceanMAE: A Foundation Model for Ocean Remote Sensing [7.641383692351135]
  Ocean remote sensing is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem monitoring.<n>We propose OceanMAE, an ocean-specific masked autoencoder that extends standard MAE pre-training by integrating multispectral Sentinel-2 observations with physically meaningful ocean descriptors during self-supervised learning.<n>Experiments show that OceanMAE yields the strongest gains on marine segmentation, while bathymetry benefits are competitive and task-dependent.
  arXiv  Detail & Related papers  (2026-04-09T12:29:41Z)
• ORCA: Object Recognition and Comprehension for Archiving Marine Species [19.88077587860992]
  ORCA is a multi-modal benchmark for marine research comprising 14,647 images from 478 species.<n>The dataset provides fine-grained visual and textual annotations that capture morphology-oriented attributes across diverse marine species.<n>Results highlight key challenges, including species diversity, morphological overlap, and specialized domain demands.
  arXiv  Detail & Related papers  (2025-12-24T12:36:57Z)
• NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding [60.76337064425815]
  We study the underwater scene understanding methods, which aim to achieve automated underwater exploration.<n>NautData is a dataset containing 1.45 M image-text pairs supporting eight underwater scene understanding tasks.<n>We propose a plug-and-play vision feature enhancement (VFE) module, which explicitly restores clear underwater information.
  arXiv  Detail & Related papers  (2025-10-31T14:00:35Z)
• UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding [54.16709436340606]
  Large vision-language models (VLMs) have achieved remarkable success in natural scene understanding.<n>Underwater imagery presents unique challenges including severe light attenuation, color distortion, and suspended particle scattering.<n>We introduce UWBench, a benchmark specifically designed for underwater vision-language understanding.
  arXiv  Detail & Related papers  (2025-10-21T03:32:15Z)
• Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset [76.92197418745822]
  camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings.<n>Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes.<n>We introduce the first underwater camouflaged instance segmentation dataset, UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations.
  arXiv  Detail & Related papers  (2025-10-20T14:34:51Z)
• MARIS: Marine Open-Vocabulary Instance Segmentation with Geometric Enhancement and Semantic Alignment [56.88334234553316]
  We introduce textbfMARIS (underlineMarine Open-Vocabulary underlineInstance underlineSegmentation), the first large-scale fine-grained benchmark for underwater Open-Vocabulary (OV) segmentation.<n>Our framework consistently outperforms existing OV baselines both In-Domain and Cross-Domain setting.
  arXiv  Detail & Related papers  (2025-10-17T07:50:58Z)
• OceanGym: A Benchmark Environment for Underwater Embodied Agents [69.56465775825275]
  OceanGym is the first comprehensive benchmark for ocean underwater embodied agents.<n>It is designed to advance AI in one of the most demanding real-world environments.<n>By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI.
  arXiv  Detail & Related papers  (2025-09-30T17:09:32Z)
• Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection [54.1960918379255]
  Neptune-X is a data-centric generative-selection framework for maritime object detection.<n>X-to-Maritime is a multi-modality-conditioned generative model that synthesizes diverse and realistic maritime scenes.<n>Our approach sets a new benchmark in maritime scene synthesis, significantly improving detection accuracy.
  arXiv  Detail & Related papers  (2025-09-25T04:59:02Z)
• SLENet: A Guidance-Enhanced Network for Underwater Camouflaged Object Detection [22.78768403870293]
  We introduce the UCOD task and present DeepCamo, a benchmark dataset designed for this domain.<n>We also propose Semantic localization and Enhancement Network (SLENet), a novel framework for UCOD.<n> Experiments on our DeepCamo dataset and three benchmark COD datasets confirm SLENet's superior performance over SOTA methods.
  arXiv  Detail & Related papers  (2025-09-04T00:44:32Z)
• Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation [85.17735565146106]
  Underwater Monocular Depth Estimation (UMDE) is a critical task that aims to estimate high-precision depth maps from underwater degraded images.<n>We develop a novel tree-aware Mamba method, dubbed Tree-Mamba, for estimating accurate monocular depth maps from underwater degraded images.<n>We construct an underwater depth estimation benchmark (called BlueDepth), which consists of 38,162 underwater image pairs with reliable depth labels.
  arXiv  Detail & Related papers  (2025-07-10T12:10:51Z)
• Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation [44.50637633194709]
  Marine Saliency (MSS) plays a pivotal role in various vision-based marine exploration tasks. We propose DiffMSS, a novel marine saliency segmenter based on the diffusion model. We develop the dedicated deterministic consensus sampling to suppress overconfident missegmentations.
  arXiv  Detail & Related papers  (2025-04-03T08:31:36Z)
• 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)
• Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Dataset [3.468621550644668]
  The maritime shipping industry is undergoing rapid evolution driven by advancements in computer vision artificial intelligence (AI) object recognition in maritime environments faces challenges such as light reflection, interference, intense lighting, and various weather conditions. Existing AI recognition models and datasets have limited suitability for composing autonomous navigation systems.
  arXiv  Detail & Related papers  (2024-07-12T05:48:53Z)
• A Computer Vision Approach to Estimate the Localized Sea State [45.498315114762484]
  This research focuses on utilizing sea images in operational envelopes captured by a single stationary camera mounted on the ship bridge. The collected images are used to train a deep learning model to automatically recognize the state of the sea based on the Beaufort scale.
  arXiv  Detail & Related papers  (2024-07-04T09:07:25Z)
• Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM [62.85895749882285]
  Marine Animal (MAS) involves segmenting animals within marine environments. We propose a novel feature learning framework, named Dual-SAM for high-performance MAS. Our proposed method achieves state-of-the-art performances on five widely-used MAS datasets.
  arXiv  Detail & Related papers  (2024-04-07T15:34:40Z)
• WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database [49.1574468325115]
  We introduce textbfWhaleNet (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations. We achieve an improvement in classification accuracy by $8-10%$ over existing architectures, corresponding to a classification accuracy of $97.61%$.
  arXiv  Detail & Related papers  (2024-02-20T11:36:23Z)
• Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons [10.75221614844458]
  We first briefly summarize four challenges for object detection on maritime UAVs, i.e. object feature diversity, device limitation, maritime environment variability, and dataset scarcity. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection.
  arXiv  Detail & Related papers  (2023-11-14T07:20:38Z)
• MarineGPT: Unlocking Secrets of Ocean to the Public [32.17362940242431]
  Large language models (LLMs) have proven to be powerful tools in promoting the user experience as an AI assistant. We propose textbfMarineGPT, the first vision-language model specially designed for the marine domain, unlocking the secrets of the ocean to the public.
  arXiv  Detail & Related papers  (2023-10-20T15:45:39Z)
• MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding [11.878077736295863]
  MarineVRS is a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.
  arXiv  Detail & Related papers  (2023-06-07T16:46:44Z)
• 1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results [152.54137779547068]
  This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.
  arXiv  Detail & Related papers  (2022-11-24T09:59:13Z)
• SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment [76.80165589520385]
  We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information. It achieves new state-of-the-art results on the challenging object detection task on both one- and two-stage detectors.
  arXiv  Detail & Related papers  (2022-03-02T04:24:05Z)

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.