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Deep Learning-Based Underwater Trash Detection System UsingYOLOv8
Summary
Researchers trained a YOLOv8 deep learning model on underwater debris imagery to enable real-time detection and classification of submerged plastic trash, demonstrating substantially faster and more scalable performance than traditional manual survey or sonar-based methods.
Plastic pollution in aquatic environments poses a significant threat to marine ecosystems, biodiversity, and human health. Traditional methods for underwater waste detection, such as manual surveys and sonar imaging, are inefficient, time-consuming, and lack scalability. This paper presents a deep learning-based system for real-time detection and classification of underwater trash using the YOLOv8 model. The proposed system is trained on a dataset comprising underwater debris images, including plastic bottles, fishing nets, glass materials, electronic waste, and other pollutants. The model is integrated into a web-based application developed using Django, enabling users to upload images and videos for detection. The system processes inputs frame-by-frame and highlights detected objects with bounding boxes and classification labels. Experimental results demonstrate that the proposed system achieves high accuracy and real-time performance even in challenging underwater conditions such as low visibility and object distortion. The solution provides an efficient and scalable approach for environmental monitoring and supports marine conservation efforts.