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Artificial Intelligence · Capstone
Microplastic Detection with Deep Learning
Detects and counts microplastic particles in microscope images
4-5 weeks build
Overview
Microscope images of filtered water samples are analysed to find and count microplastic particles. A YOLOv8 detector trained on a public microplastic image dataset localises particles and sorts them by shape into fragments, fibres and films, and a size estimate from a scale calibration produces a per-sample distribution a lab can compare across sites.
What makes it stand up
- 01Particle detection on microscope images
- 02Shape classes for fragments, fibres and films
- 03Pixel-to-micron calibration for size estimates
- 04Per-sample counts and size histograms
- 05Detection metrics and manual-count comparison in the report
Module breakdown
- 01
Imaging prep
Contrast normalisation and tiling of large images.
- 02
Detector
YOLOv8 trained for particle shape classes.
- 03
Calibration
Scale bar reading for size in microns.
- 04
Analysis
Counts, size bins and sample comparison.
- 05
Lab app
Streamlit upload with annotated results and CSV.
After this, you will be able to
- Explain tiling and why small objects need it
- Defend your calibration and size estimation
- Demo a sample image turned into particle counts
Technology stack
- Python
- PyTorch
- YOLOv8
- OpenCV
- Streamlit
- Pandas
- Level
- Capstone
- Domain
- Artificial Intelligence
- Build time
- 4-5 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support