Underwater Net: Efficient Visual Detection Of Marine Garbage For Eco Monitoring
DOI:
https://doi.org/10.63665/IJMEC.1104.02Keywords:
Marine Pollution, YOLOv10n, Underwater Garbage Detection, Object Detection, Deep Learning, IoT, Real-Time Monitoring, Underwater Robotics.Abstract
Marine pollution poses a severe threat to the sustainability of aquatic ecosystems and the blue economy. Effective detection and classification of underwater debris are crucial for enabling timely interventions and supporting marine conservation efforts. In this project, we present an advanced underwater garbage detection system based on YOLOv10n, a cutting-edge, lightweight object detection model optimized for resource-constrained IoT and underwater robotic platforms. Building on the challenges identified in traditional detection models—such as high computational costs and deployment complexity—we replace older backbones like CSPDarknet with the more efficient YOLOv10n architecture. YOLOv10n is designed with an emphasis on speed, low parameter count, and high accuracy, making it ideal for real-time underwater applications. Our system achieves robust debris detection with high precision, while significantly reducing memory and processing requirements, thereby facilitating deployment on embedded and mobile devices. This project demonstrates the feasibility and effectiveness of using YOLOv10n for scalable and eco-friendly marine monitoring solutions, providing a practical approach to combat marine pollution through intelligent automation.
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