Rapid detection of microfibres in environmental samples using open-source visual recognition models
Stamatia Galata, Ian Walkington, Timothy Lane, Konstadinos Kiriakoulakis, Jonathan Dick
Abstract
Microplastics, particularly microfibres (< 5 mm), are a significant environmental pollutant. Detecting and quantifying them in complex matrices is challenging and time-consuming. This study presents two open-source visual recognition models, YOLOv7 and Mask R-CNN, trained on extensive datasets for efficient microfibre identification in environmental samples. The YOLOv7 model is a new introduction to the microplastic quantification research, while Mask R-CNN has been previously used in similar studies. YOLOv7, with 71.4 % accuracy, and Mask R-CNN, with 49.9 % accuracy, demonstrate effective detection capabilities. Tested on aquatic samples from Seyðisfjörður, Iceland, YOLOv7 rapidly identifies microfibres, outperforming manual methods in speed. These models are user-friendly and widely accessible, making them valuable tools for microplastic contamination assessment. Their rapid processing offers results in seconds, enhancing research efficiency in microplastic pollution studies. By providing these models openly, we aim to support and advance microplastic quantification research. The integration of these advanced technologies with environmental science represents a significant step forward in addressing the global issue of microplastic pollution and its ecological and health impacts. • The study uses YOLOv7 and mask R-CNN to detect microplastic fibres. • Accuracies were 71.4 % (YOLOv7) and 49.9 % (mask R-CNN) for environmental samples. • Both offer rapid quantification of microfibres (0.4-0.6 fibres per second). • Both models are open source, and usable with limited knowledge of coding. • Speed and accuracy highlight use in environmental microplastic fibre detection.