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

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@article{1724560,
      author = {Aarya C. Meshram, Ritesh R. Tarawadekar, Tanishk S. Shinde, Prajakta K. Bujare},
      title = {AquaGuard: AI-Assisted Underwater Marine Debris Detection and Reporting — A Literature Review and System Design},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {329-341},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724560.pdf},
      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.},
      keywords = {marine debris; underwater object detection; YOLO; human-in-the-loop review; environmental monitoring; Flask; Flutter},
      month = {November},
  }