aceDeveloping and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization
Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…
Domain-specific EfficientNet-B0 classifier and Claude Vision API with human-in-the-loop guidance automated morphological characterization of microplastics from optical microscope images, achieving high F1-scores for shape/type, color and texture.
Evaluation relied on a small in-house dataset of approximately 700 annotated images and VLM performance was substantially lower without expert guidance, limiting generalizability to large-scale monitoring.
Evidence
- Peer-reviewedScientific Reports2026-08-25
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Truvace Impact Record TRV-2026-0894, v1: “Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization.” Truvace, 2026-08-26. /record/TRV-2026-0894 (accessed at citation time). sha256 068a458800f3bae8…
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