aceDevelopment of a YOLO-guided automated (microplastic) particle analysis workflow
Microplastics (MPs) are an emerging pollutant of global concern, creating an urgent need for rapid and accurate monitoring workflows. Deep learning-based computer vision has demonstrated strong performance in finding particles in microscopy images, but its use as a front-end module for automated IR/Raman microscope-based MP analysis remains insufficiently developed, particularly in workflows that convert image-level particle detection results into microscope-executable operations for particle morphological chara…
A YOLO11 instance segmentation model found reference microplastic particles in microscope images with an F1 of 0.89, beating a threshold method (0.25) and Mask R-CNN (0.84), and was built into an automated analysis workflow.
Particle detection performance dropped when image backgrounds were dirtier, as seen in commercial salt samples, which limits reliability on messy real samples.
Detection performance decreased with dirtier image backgrounds, as shown on a commercial salt sample, so the workflow's accuracy is weaker on less clean real-world samples.
Evidence
- Peer-reviewedAnalytical Methods2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1989, v1: “Development of a YOLO-guided automated (microplastic) particle analysis workflow.” Truvace, 2026-10-20. /record/TRV-2026-1989 (accessed at citation time). sha256 9367ab4af48c899b…
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