# An AI-Powered Autonomous Underwater System for Sea Exploration and Scientific Research

arXiv: https://arxiv.org/abs/2512.07652 · HTML: https://mlsanity.com/paper/2512.07652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis](https://mlsanity.com/paper/2008.12603/) (2020)
- [MarineGPT: Unlocking Secrets of Ocean to the Public](https://mlsanity.com/paper/2310.13596/) (2023)
- [Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study](https://mlsanity.com/paper/2401.02147/) (2024)
- [Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models](https://mlsanity.com/paper/2503.17142/) (2025)
- [Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation Strategies](https://mlsanity.com/paper/2403.07113/) (2024)
- [YOLOv12: A Breakdown of the Key Architectural Features](https://mlsanity.com/paper/2502.14740/) (2025)
- [Energy Management for Autonomous Underwater Vehicles Using Economic Model Predictive Control](https://mlsanity.com/paper/1906.08719/) (2019)
- [MERLION: Marine ExploRation with Language guIded Online iNformative Visual Sampling and Enhancement](https://mlsanity.com/paper/2503.06953/) (2025)

## Abstract (arXiv)

Traditional sea exploration faces significant challenges due to extreme conditions, limited visibility, and high costs, resulting in vast unexplored ocean regions. This paper presents an innovative AI-powered Autonomous Underwater Vehicle (AUV) system designed to overcome these limitations by automating underwater object detection, analysis, and reporting. The system integrates YOLOv12 Nano for real-time object detection, a Convolutional Neural Network (CNN) (ResNet50) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and K-Means++ clustering for grouping marine objects based on visual characteristics. Furthermore, a Large Language Model (LLM) (GPT-4o Mini) is employed to generate structured reports and summaries of underwater findings, enhancing data interpretation. The system was trained and evaluated on a combined dataset of over 55,000 images from the DeepFish and OzFish datasets, capturing diverse Australian marine environments. Experimental results demonstrate the system's capability to detect marine objects with a mAP@0.5 of 0.512, a precision of 0.535, and a recall of 0.438. The integration of PCA effectively reduced feature dimensionality while preserving 98% variance, facilitating K-Means clustering which successfully grouped detected objects based on visual similarities. The LLM integration proved effective in generating insightful summaries of detections and clusters, supported by location data. This integrated approach significantly reduces the risks associated with human diving, increases mission efficiency, and enhances the speed and depth of underwater data analysis, paving the way for more effective scientific research and discovery in challenging marine environments.