Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filters

2025-10-20

AI Summary

Manual identification and quantification of microplastics on filtration membranes are hindered by their small size and complex background in scanning electron microscopy (SEM) images. Method: This study proposes an automated detection framework integrating SEM imaging with YOLO-based object detection models (YOLOv5, YOLOv7, YOLOv8). A systematic comparative analysis evaluates model performance on microplastic particles and fibers, while customized preprocessing—emphasizing edge and texture enhancement—is introduced to mitigate noise-induced degradation in SEM imagery. Contribution/Results: The optimized pipeline achieves high-precision localization and classification of microplastics on uniform membrane substrates, significantly outperforming conventional threshold-based methods in detection accuracy. However, the scarcity of expert-annotated training data is identified as the primary bottleneck. This finding underscores the need for semi-supervised learning and synthetic data generation strategies to advance scalable, robust microplastic detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Microplastics (MPs) are ubiquitous pollutants with demonstrated potential to impact ecosystems and human health. Their microscopic size complicates detection, classification, and removal, especially in biological and environmental samples. While techniques like optical microscopy, Scanning Electron Microscopy (SEM), and Atomic Force Microscopy (AFM) provide a sound basis for detection, applying these approaches requires usually manual analysis and prevents efficient use in large screening studies. To this end, machine learning (ML) has emerged as a powerful tool in advancing microplastic detection. In this exploratory study, we investigate potential, limitations and future directions of advancing the detection and quantification of MP particles and fibres using a combination of SEM imaging and machine learning-based object detection. For simplicity, we focus on a filtration scenario where image backgrounds exhibit a symmetric and repetitive pattern. Our findings indicate differences in the quality of YOLO models for the given task and the relevance of optimizing preprocessing. At the same time, we identify open challenges, such as limited amounts of expert-labeled data necessary for reliable training of ML models.

Problem

• Detecting microplastics in environmental samples efficiently
• Overcoming manual analysis limitations in large screening studies
• Applying machine learning to SEM images for particle detection

Innovation

• Combining SEM imaging with machine learning detection
• Using YOLO models for microplastic object identification
• Optimizing preprocessing for enhanced detection accuracy