Abstract :

The Viability Of Aquatic Ecosystems And The Blue Economy Are Seriously Threatened By Marine Pollution. Underwater Debris Must Be Effectively Detected And Classified In Order To Facilitate Prompt Interventions And Aid In Marine Conservation Initiatives. In This Research, We Offer A Sophisticated Underwater Rubbish Detection System Built On Top Of YOLOv10n, A State-of-the-art, Lightweight Object Detection Model Tailored For IoT And Underwater Robotic Platforms With Limited Resources. We Replace Earlier Backbones Like CSPDarknet With The More Effective YOLOv10n Architecture, Building On The Issues Found In Classic Detection Approaches, Such As High Computing Costs And Deployment Complexity. YOLOv10n Is Perfect For Real-time Underwater Applications Because Of Its Excellent Accuracy, Minimal Parameter Count, And Emphasis On Speed. Our Technology Enables Deployment On Embedded And Mobile Devices By Achieving Reliable Debris Identification With High Precision While Drastically Lowering Memory And Processing Requirements. This Study Offers A Workable Strategy To Address Marine Pollution Through Intelligent Automation, Demonstrating The Viability And Efficacy Of Deploying YOLOv10n For Scalable And Environmentally Friendly Marine Monitoring Solutions.

Published:

14-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

871-876

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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