Detection of microfibres in wastewater sludge with deep learning

The proliferation of microplastics, especially microfibres (MFi), represents a significant environmental concern due to their persistence and potential health risks. In particular, wastewater treatment plants are among the primary contributors to the release of MFi into ecosystems, making it crucial to detect and quantify these pollutants, mainly in sewage sludge. Effective monitoring not only helps assess the extent of pollution but also identifies key sources. Traditional detection methods are labour-intensive and lack the scalability necessary for effective monitoring. This study presents a novel approach utilising advanced deep learning techniques to enhance the detection of MFi in sewage sludge samples using two different filtration support (fibreglass and cellulose acetate). By leveraging convolutional neural networks (CNNs), we developed a robust system for accurately identifying and localising microfibres. Our deep learning framework, implemented using Mask R-CNN architecture, demonstrates superior performance in detecting MFi, achieving a mean average precision (mAP) of 72% for the glass dataset and 68% for the cellulose acetate dataset. This approach significantly reduces manual effort and processing time. However, further improvements are still possible. as our model shows weaker performance for smaller fibres and those that resemble fibrous morphology (particularly on cellulose acetate filters). Future work could address these issues by expanding the presented datasets to address the identified shortcomings. • Developed two extensive datasets with over 1,200 annotated images each, enabling effective detection and segmentation of microfibres in wastewater sludge using advanced imaging techniques. • Implemented Mask R-CNN architecture to achieve precise microfibre identification, overcoming limitations of traditional manual or spectroscopic methods. • The proposed model significantly reduces processing time and manual labour, achieving average processing times of under 4 seconds per image on standard computing hardware. • Introduced a user-friendly desktop application, allowing researchers to leverage the trained model for microfibre detection in sludge samples without requiring programming expertise.