We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
AI summary Read the abstract
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.
Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 – Clean Water and Sanitation, promoting sustainable management of water.
More Papers Like This
Efficient and accurate microplastics identification and segmentation in urban waters using convolutional neural networks
AI summary Read the abstract
Researchers developed convolutional neural network models for efficiently identifying and segmenting microplastics in urban water samples from southern China. The study found that deep learning approaches can significantly reduce the time and labor required for microplastic identification compared to manual methods, offering a scalable tool for monitoring microplastic pollution in urban waterways.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
AI summary Read the abstract
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.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
AI summary Read the abstract
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.
Automatic Detection of Microplastics in the Aqueous Environment
AI summary Read the abstract
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.
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.