• B Chandrashekar reddy
• Beulah J Karthikeyan
• D Srimadhan
• G Sainath
• V Shiva Kumar Chary

Marine ecosystems worldwide face critical degradation from plastic pollution threatening biodiversity and ecological functioning this research introduces a deep learning application utilizing yolov8 architecture for underwater plastic waste detection and classification our project demonstrates robust performance in varied aquatic conditions enabling real-time identification of diverse plastic debris types the technology offers substantial benefits for marine conservation efforts through enhanced monitoring capabilities pollution source tracking and data-driven cleanup strategy optimization experimental results show significant improvements in detection accuracy compared to previous approaches particularly in challenging underwater visibility scenarios this work contributes to the growing field of ai-supported environmental monitoring and presents a scalable tool for quantitative assessment of marine plastic pollution to inform both conservation practice and environmental policy development.

Plastic Detection, Debris, Classification, YOLOv8, Segmentation, Computer Vision, Deep Learning

"Deep Learning Based Underwater Trash Detection System Using YOLOv8", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.g357-g361, March-2025, Available :http://www.jetir.org/papers/JETIR2503640.pdf

"Deep Learning Based Underwater Trash Detection System Using YOLOv8", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppg357-g361, March-2025, Available at : http://www.jetir.org/papers/JETIR2503640.pdf