Nep & trash count
Count neps, seed-coat fragments, and trash per gram of cotton web, sized and classified.
Example outputs shown for illustration. Numbers depend on your samples and protocol.
Image: Illustrative rendering (AI-generated, gpt-image-2), not a micrograph of a specific sample
Results
Each measurement below comes from your own images, tied back to the annotated frame it came from, so results stay comparable across samples, locations, and conditions.
µm or count
Neps per gram
Size and shape measured for every detected object, then summarized across the population.
Example, not a claimed result
In your report: Per-object table, size distribution, and summary percentiles.
µm or count
Trash per gram
Size and shape measured for every detected object, then summarized across the population.
Example, not a claimed result
In your report: Per-object table, size distribution, and summary percentiles.
µm or count
Size distribution
Size and shape measured for every detected object, then summarized across the population.
In your report: Per-object table, size distribution, and summary percentiles.
class
Nep vs trash class
Each detected feature assigned to a class using the agreed rule set, shown on the image.
Example, not a claimed result
In your report: Per-feature class, counts by class, and annotated overlays.
per area
Web position map
Findings placed in sample coordinates to reveal spatial patterns and local density.
In your report: Coordinate-referenced map, regional density, and representative images.
Built around your image set
Use the calibrated images your team already collects, together with the locations you need to compare.
Each report includes
The artifacts your team receives, ready for review and archive.
Annotated image set
Original images with regions of interest, measurement points, masks, and finding overlays.
Measurement table
Location-indexed values, units, distributions, and QC flags in a structured export.
Method record
Calibration evidence, analysis settings, version history, and validation summary.
Confidence in every resultTraceable measurements, reviewed against your agreed reference method.
- Traceable scale
- The report records the image scale, calibration evidence, and a method-specific expanded measurement uncertainty. It does not use one product-wide accuracy number.
- Reference comparison
- The configured method is compared with your accepted reference method or reviewed annotations, and reports bias by measurement range and image condition.
- Detection performance
- Detections are evaluated against reviewed reference regions with precision, recall, and segmentation overlap, separated by the conditions that affect performance.
- Repeatability
- The locked protocol is rerun on the same inputs and on a defined repeat set. The review records variation from image acquisition, sampling, and analysis separately where possible.
What this result does not establish: Counts and sizes objects visible on the imaged web surface. The split between neps, seed-coat fragments, and trash follows size and reflectance; confirm ambiguous objects by microscopy.
The measurement, today
Neps and trash are counted by hand off a card web, or scored against reference cards. Sampling is small, counting is slow, and the small neps that matter most are the easiest to miss.
What it costs
Neps survive into the yarn and show up as dye-resist specks in finished fabric. High trash raises ends-down at the spinning frame and pushes card waste up, so the cost lands twice.
From image to reviewed result
- 1
Image the web
Scan a card web or a prepared web sample against a contrasting backing.
- 2
Detect the objects
Every nep, seed-coat fragment, and trash particle is segmented and located.
- 3
Size and separate
A size threshold separates neps from larger trash, the same filter that isolates small objects elsewhere in the engine.
- 4
Normalise per gram
Counts are divided by the sampled web mass and reported with the full size distribution.
Not sure this is the right measurement?
Send a representative image and your measurement goal. A ConductVision scientist will confirm whether this is the right fit, or point you to the closer workflow, before you commit to a quote.
Related applications
Fiber diameter distribution
Mean diameter, CV%, and D10/D50/D90 for wool, synthetic, and nanofiber, measured from the image.
Particle size analysis
Static image-analysis particle sizing with shape descriptors across full fields.
Fiber & inclusion analysis
Count and size fibers and inclusions, with area fraction and class breakdowns.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
