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Advancing marine microplastic monitoring through deep learning-based image segmentation
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Scientists in Tunisia trained an AI tool to spot and identify tiny plastic fragments on beaches by analyzing photos, sorting them into types like the plastics used in packaging and bottles. This matters because microplastics are piling up in our oceans and can end up in seafood we eat, so having a faster, more accurate way to track different plastic types could help researchers monitor pollution and understand its risks to both marine life and human health. The tool isn't perfect yet, but it shows AI can make this kind of large-scale environmental monitoring more practical.
Abstract Microplastic pollution poses significant threats to marine ecosystems and human health, requiring efficient and standardized monitoring within a One Health framework. This study presents the development and evaluation of an artificial intelligence (AI)-driven image segmentation model for detecting microplastics in images collected along the coastline of Sousse, Tunisia, a region particularly vulnerable to high pollution. The dataset includes 1080 images of plastics categorized into classes: high-density polyethylene (HDPE), low density polyethylene (LDPE), polyamide (PA), polyethylene terephthalate (PET), polypropylene (PP), and polystyrene (PS), as well as an "other" category comprising non-plastic and miscellaneous beach-collected items, such as cigarette butts and dried Posidonia oceanica . To ensure reliability with AI analysis, all fragments were first identified, photographed under controlled lighting to capture morphological variability. Using advanced polygon segmentation, the model enables pixel-level annotation and outperforms traditional bounding box methods, especially for irregularly shaped particles. Background subtraction and adaptive thresholds enhance accuracy, reducing false positives. Model performance is evaluated using the Intersection-over-Union (IoU) metric, measuring overlap between predicted segmentations and manually annotated ground truth data. Additionally, it discusses the ecological relevance of identified microplastic types in the regional context, highlighting potential impacts on coastal biodiversity and environmental health. Findings contribute to the growing field of marine pollution monitoring by demonstrating the practical utility and challenges of AI-powered segmentation, supporting future efforts toward scalable, ecologically informed, and standardized microplastic detection methods.
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