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Automatic Detection of Microplastics in the Aqueous Environment
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Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Microplastics (<5 mm) have become a global concern due to their growing threat to the marine and freshwater environment. There is a lack of technologies for the rapid and accurate identification and quantification of microplastics in the aqueous environment. This paper presents a deep-learning-based methodology for real-time detection, tracking, and counting of microplastics in freshwater environments through real-time object detection. A prototype was developed to detect microplastics of 1 mm to 5 mm in size and different shapes (e.g., spherical) and colors (e.g., red, green, blue). The microplastics detection model employed the small YOLOv5 architecture as we focused on low-power applications. In-situ image collection was performed using a Logitech C270 camera, and the microplastics were manually annotated on those images before being applied for model training. For real-time object tracking, we used Simple Online and Real-time Tracking with a Deep Association Metric (DeepSORT), an extended version of the Simple Online and Real-time Tracking (SORT) algorithm. Our developed system can work up to 34 cm/sec of water velocity and successfully detect, track, count, and calculate the velocity of microplastic of size 5mm.
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This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
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This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
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Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Deep Learning-Based Prediction and Classification of Microplastics in Water Samples
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Researchers built an AI tool that can automatically spot and identify tiny plastic particles in drinking water samples, correctly catching them about 92% of the time — much faster than manual lab methods. This matters because it could help water utilities quickly find contamination hotspots and monitor water quality in real time, making it easier to protect drinking water from microplastic pollution before it reaches your tap.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
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Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.