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MicroFiberDetect: An Application for the Detection of Microfibres in Wastewater Sludge Based on CNNs
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MicroFiberDetect is a deep-learning desktop application designed to automatically identify and measure microfibres in wastewater sludge samples, replacing the slow and labor-intensive manual analysis currently used. The tool uses convolutional neural networks to detect fibres and record their size and color, significantly speeding up the analysis process. Automated tools like this are important for tracking the microfiber pollution coming from textiles through wastewater treatment systems.
Microplastics and microfibres are now widespread in aquatic ecosystems, as oceans and rivers. A serious portion of these microplastics come from urban wastewater treatment plants. Traditional methods for detecting and quantifying them are labour-intensive and time-consuming. This paper introduces MicroFiberDetect, a novel application designed to enhance the detection and quantification of microfibres within sludge samples. Leveraging the power of deep learning, this innovative tool provides detection accuracy and insights into the size and colour of each identified fibre. Reducing time and manpower required for analysis while increasing accuracy and throughput. The application has been deployed as a desktop application that allows field experts to quantify and analyse microfibres in sludge samples.
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Researchers developed a deep learning system using convolutional neural networks to automatically detect microfibres in sewage sludge samples, achieving detection accuracy of 68-72% depending on the filter type used. This approach significantly reduces the manual labor and processing time traditionally required to identify microplastic contamination in wastewater. The technology could help scale up monitoring of microfibre pollution from wastewater treatment plants, which are among the primary sources of environmental microfibre release.
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