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YOLOv8-based Deep Learning for Ocean Plastic Detection
Summary
Scientists trained a smart AI system to automatically spot and identify plastic trash in ocean photos, and it worked with high accuracy. This matters because plastic pollution doesn't just harm sea life—it breaks down into microplastics that end up in our seafood, water, and eventually our bodies. Faster, cheaper, large-scale plastic tracking like this could help us clean up oceans before that pollution makes its way onto our dinner plates.
The rising numbers of plastic in the oceans and coastlines is an increasing global crisis. Plastic affects not only marine wildlife, but affects all of us. This study describes the use of a deep learning object detection system using the YOLOv8 algorithm to detect and identify the different types of plastic in the ocean based on real-time images. Traditional methods of monitoring plastic in the ocean have limited capability to provide reliable, scalable, and cost-effective means to locate and quantify plastic in the ocean. This detection system will provide these capabilities in addition to providing high accuracy. The model is trained and validated on a data set containing more than 2,000 annotated photographs of different types of marine debris. The results of this research shows that the proposed detection system performs with a high degree of accuracy in the application of large scale monitoring of marine debris by measuring precision, recall, and mAP performance in terms of detecting marine debris.