YOLO-Aqua: Intelligent Detection of Plastic Debris in Aquatic Environments

Tanisha Dutta; C Sona Vijayan

doi:10.71097/IJSAT.v16.i4.9434

YOLO-Aqua: Intelligent Detection of Plastic Debris in Aquatic Environments

Author(s)	Ms. Tanisha Dutta, Ms. C Sona Vijayan
Country	India
Abstract	The use of plastic in water bodies has become a major global environmental problem, threatening biodiversity and disrupting food chains in the oceans. Conventional methods of detecting plastic waste in water are often ineffective, as they rely heavily on manual input, especially in shifting and complex underwater environments. This project proposes an automated and intelligent system for detecting underwater plastic. It integrates the state-of-the-art object detection model YOLOv8 with Particle Swarm Optimization (PSO). YOLOv8 enables real-time detection with a lightweight, anchor-free architecture, making it effective in identifying irregularly shaped and partially concealed plastic debris in underwater video streams. However, its performance declines when hyperparameters are manually tuned. To address this, PSO optimizes critical parameters such as learning rate, confidence threshold, and anchor box sizes, thereby improving both detection accuracy and model robustness. The system leverages PSO to extract relevant features for efficient training, while YOLOv8 identifies plastic waste in real time. A Convolutional Neural Network (CNN) is employed to detect various types of debris, including bottles, bags, nets, and covers. This approach delivers low latency, higher classification accuracy, and reduced false positives, even under murky conditions. Experimental results on standard datasets demonstrate that this method outperforms traditional YOLO models, achieving significant improvements in accuracy, precision, recall, and F1 score. The proposed system can be deployed in real time on autonomous underwater vehicles (AUVs) and large-scale ocean surveillance platforms to track plastic and support effective ocean cleanup efforts. By combining deep learning with environmental sustainability, this project provides a scalable solution to address marine pollution while advancing global conservation goals.
Keywords	Underwater Plastic Detection, YOLOv8, Particle Swarm Optimization (PSO), Deep Learning, Object Detection, Marine Pollution, CNN, Autonomous Underwater Vehicles (AUVs), Real-Time Detection, Environmental Sustainability, Plastic Waste Identification, Feature Optimization, Image Processing, Marine Conservation
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-11-21
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9434
Short DOI	https://doi.org/hbb8g7

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 16 Isu 3 Cover Page Vol 16 Isu 2 Cover Page Vol 16 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

YOLO-Aqua: Intelligent Detection of Plastic Debris in Aquatic Environments

Share this