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Detection and Classification of Floating Waste on Water Surfaces Using YOLO-Based Algorithms
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Researchers trained AI models to spot floating trash—like plastic bottles, bags, and cans—in water using photos, with their best system (YOLOv11) correctly identifying waste about 83% of the time. This kind of technology could help clean-up crews find and remove plastic pollution before it breaks down into microplastics, which can end up in the fish and water we consume and have been linked to potential health concerns.
Floating waste on water surfaces is one of the causes in environmental pollution. Foam, plastics, and glass bottles are not readily biodegradable and adversely affect aquatic life. This research focuses on detecting, classifying, and counting non-biodegradable and hard-to-decompose waste floating on water surfaces using object detection techniques. Five classes of waste, cans, foam, plastic bags, plastic bottles, and miscellaneous items, are evaluated in this study. Detection and classification are performed by using six models-YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLOv11-which can identify overlapping objects. This study utilizes two datasets with varying resolutions, as well as two model sizes and two batch sizes. In the experimental evaluation, the YOLOv11 model outperforms the other models with a precision of 83% and mAP50 of 78%. Plastic bags and plastic bottles are classified more accurately by YOLOv11 than by YOLOv5, with improvements of 22% and 15%, respectively.
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Deep Learning-Based Underwater Trash Detection System UsingYOLOv8
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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.
Ocean and River Plastic Waste Detection Using Computer Vision
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Millions of tons of plastic pour into our oceans every year, much of it carried by rivers—but tracking where it comes from has always relied on slow, manual surveys. This study built an AI camera system that automatically spots and counts floating plastic waste from drone and shoreline footage with over 91% accuracy, offering a faster way to find pollution hotspots. Better tracking matters because plastic waste that lingers in waterways breaks down into microplastics, which end up in the fish and water we consume, making tools like this useful for guiding cleanup efforts before pollution reaches our food and water supply.
Real-Time Riverine Plastic Waste Detection Using an Enhanced YOLO Framework
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Researchers built an AI tool that can automatically spot plastic waste in rivers with about 95% accuracy, using smart camera technology similar to what's used in self-driving cars. This matters because plastic trash in rivers eventually breaks down into microplastics that can end up in our drinking water and food supply—catching it early and tracking pollution hotspots could help communities clean up waterways before that happens.
YOLOv8-based Deep Learning for Ocean Plastic Detection
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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.
Marine Debris Recognition with Artificial Intelligence
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Millions of tons of plastic enter our oceans every year, breaking down into tiny microplastics that work their way up the food chain and onto our plates. Researchers built an AI tool that scans underwater photos and automatically spots trash with about 82% accuracy, which could help cleanup groups and governments find and target pollution hotspots faster than manual monitoring allows. This kind of tech won't solve ocean plastic pollution on its own, but it's a practical step toward tracking the problem that ultimately threatens seafood safety and human health.
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