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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.
Plastic pollution in aquatic ecosystems has emerged as one of the most pervasive environmental crises of the modern era, with an estimated 8 to 11 million metric tonnes of plastic waste entering the world's oceans annually, a substantial proportion of which is transported through river systems originating from inland sources. The Central Pollution Control Board of India identifies the Ganga, Cauvery, and several peninsular river systems as significant contributors to marine plastic loading, with riverine plastic transport patterns showing strong seasonal correlation with monsoon-driven surface runoff. Conventional plastic pollution monitoring relies on manual shoreline surveys and periodic sampling, both of which are labour-intensive, spatially sparse, and unable to provide the continuous, wide-area coverage necessary for effective pollution source tracking and cleanup resource allocation. This paper presents a computer vision-based framework for automated detection, classification, and density estimation of floating plastic waste in river and coastal water bodies, combining a fine-tuned YOLOv8 object detection model trained on a composite dataset of aerial drone imagery and fixed shoreline camera footage with a downstream density mapping module that converts per-frame detections into spatially and temporally resolved pollution density maps. The detection model is trained to distinguish seven plastic waste categories (PET bottles, plastic bags, styrofoam, multilayer packaging, fishing net debris, plastic containers, and miscellaneous plastic fragments) from natural floating debris such as leaves, wood, and algae mats that commonly cause false positives in aquatic object detection. Field validation conducted along a 12-kilometre stretch of the Adyar River in Chennai and a coastal stretch near Marina Beach demonstrates a mean detection accuracy (mAP@0.5) of 91.4%, real-time inference at 28 frames per second on edge GPU hardware suitable for drone deployment, and density mapping correlation of r=0.87 against manual ground-truth quadrant sampling, establishing the framework's viability as a scalable monitoring tool for river and coastal plastic pollution management authorities. Keywords — Plastic Pollution Detection, Computer Vision, YOLOv8, Marine Debris, River Monitoring, Aerial Drone Imagery, Object Detection, Environmental Monitoring, Density Mapping, Aquatic Ecosystem
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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.
Leveraging Artificial Intelligence for Plastic Waste Management in Coastal Waters: A Drone-Based Approach for Identification and Hotspot Detection
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A machine learning model trained on drone footage achieved 89.4% accuracy in classifying plastic debris types in coastal waters and identifying hotspot areas for targeted cleanup operations. The AI-driven approach offers faster and more scalable monitoring than traditional methods, though detection of small microplastics and environmental variability remain challenges.
Designing Realtime Object Detection Based Plastic Litter Monitoring System
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Researchers built an automated camera system that uses AI to detect and count plastic waste floating in rivers, offering a faster alternative to the current method in Indonesia, which relies on people manually watching and counting debris. This matters because rivers are a major pathway for plastic pollution into the ocean, and better tracking tools can help pinpoint pollution sources and guide cleanup efforts before the plastic breaks down into microplastics that contaminate water, food, and potentially our bodies. While the AI system slightly undercounted debris compared to human observers, it offers the advantage of continuous, real-time monitoring that isn't practical
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.
Marine Debris Detection Using Artificial Intelligence
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Every year, about 11 million tons of plastic pours into our oceans, 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 marine trash with about 82.5% accuracy, then packaged it into a free web platform so cleanup groups and governments can find and target pollution hotspots faster. Catching this waste before it fragments further could help slow the flow of microplastics into seafood and, ultimately, into us.
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