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FCA-YOLOv8n: Frequency-Augmented Lightweight Network for River Plastic Waste Detection
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Researchers built a lightweight AI system that drones can use to spot plastic trash floating in rivers—even small, clear, or shiny pieces that are normally hard to see—while running efficiently enough for real-world use. This matters because catching plastic waste in rivers before it breaks down into microplastics could help reduce the amount that ends up in our water, food, and eventually our bodies, where microplastics have raised health concerns. While this study focuses on detection technology rather than health outcomes directly, better tracking tools are a key step toward cleaning up waterways more effectively.
To address the detection challenges of riverine plastic debris from UAV perspectives—characterized by small size, high reflectivity, and semi-transparency—this paper proposes the Frequency-Spatial Collaborative Detection Network, FCA-YOLOv8n. The network reconstructs the detection head by introducing a high-resolution P2 layer and removing the redundant P5 layer, which compresses the parameter count from $ ext{3. 0 M}$ in the original YOLOv8n to $ ext{1. 2 M}$ (a $ ext{6 0 \%}$ reduction). Furthermore, it incorporates three key modules: a Frequency-selective Spatial Fusion Module (Fre-SPPF) to suppress water wave noise, a Multi-form Adaptive Attention Module (DyLG-C2f) to capture multi-scale features, and a Semi-transparent Object Fine Reconstruction Upsampling Module (FAF-Dysample) to restore faint contours. Experiments on our self-built dataset demonstrate that FCA-YOLOv8n achieves a real-time speed of 32.0 FPS, with mAP @ 0.5 reaching 94.53% and mAP @ $0.5: 0.95$ reaching 67.17 %. Compared to the baseline YOLOv8n, our model improves $ ext{mAP} {@} 0.5$ and $ ext{mAP} {@} 0.5: 0.95$ by 2.93 % and 2.80 %, respectively. This approach significantly mitigates missed and false detections in complex aquatic environments, offering an efficient and precise solution for resource-constrained UAV devices.
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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.
Multi-Scale Hierarchical Attention Ensemble Network for Fine-Grained Riverine Waste Segmentation Using UAV Multispectral Imagery
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Researchers used a drone with a special five-band camera to fly over a river in Indonesia and trained an AI system to automatically spot and map plastic waste floating in the water, even distinguishing it from natural debris like leaves and branches. This matters because rivers are a major pathway for plastic to reach oceans and break down into microplastics, which can end up in our food and water; better, faster detection tools like this could help communities target cleanup efforts before waste spreads further. The study is an early proof-of-concept from one river during one season, so more testing is needed before it can be used widely.
Rapid and Accurate Quantification of Microplastics in Surface Water using Improved YOLO Model
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Scientists developed a faster, more accurate AI tool to detect and count tiny plastic particles (microplastics) in water samples. This matters because microplastics are showing up everywhere, including our drinking water, and better detection tools help researchers track this pollution more efficiently, which is a key step toward understanding its health risks and creating solutions to reduce our exposure.
Improved Transformer-based detection of underwater plastic debris in complex environments
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Researchers built an improved AI system that can spot plastic trash underwater more accurately, even in murky, cluttered footage where debris is small or hard to see. This matters because better detection tools could help track and clean up ocean plastic before it breaks down into microplastics — the tiny particles increasingly found in seafood, drinking water, and even human blood and organs. While this study focused on detection technology rather than health outcomes directly, improving our ability to find and remove plastic waste is an important step toward reducing microplastic pollution that can ultimately end up in our food chain and bodies.
A hybrid attention-based deep learning framework for high-accuracy microplastic detection and monitoring in water environments
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Scientists built a smarter AI tool that can spot tiny plastic particles in water more accurately, even under tricky conditions like murky water or changing light. This matters because microplastics are increasingly found in our drinking water and food supply, and better detection tools could help researchers and water treatment facilities identify contamination faster and more reliably, ultimately supporting efforts to keep our water supplies safer.
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