Work

AquaGuard: AI-Assisted Underwater Marine Debris Detection and Reporting — A Literature Review and System Design

Year: 2026

Type: article

Abstract: Underwater marine debris is hard to monitor because observations are gathered with different tools, judged by different people and stored in formats that cannot be compared. Deep-learning detectors have been applied to sonar, remotely operated vehicle (ROV) and towed-camera imagery, yet the work reviewed here concentrates on detection models and annotated datasets rather than on the workflow that surrounds a detection. This paper surveys research on marine-debris detection and monitoring, compares existing approaches by sensing modality, detector family, data and workflow support, and derives six research gaps. It then presents AquaGuard, a modular system in which a single-stage object detector sits behind a Flask backend with role-based access, a SQLite store, reviewer-in-the-loop correction and shared web and Flutter mobile front ends. The contribution is a design argued from the literature: no detection accuracy is reported, and the held-out evaluation that must precede any performance claim is specified. Development status and open risks, including domain shift and false positives from natural background, are discussed.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/ire1724560

Authors: Aarya C. Meshram, Ritesh R. Tarawadekar, Tanishk S. Shinde, Prajakta K. Bujare

Institutions: Sanjay Ghodawat University, Sanjay Ghodawat Institute

Language: English

Topic: Microplastics and Plastic Pollution

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life below water, Clean water and sanitation

Open Access status: bronze

OpenAlex ID: W7225077584