Zero miscounts
Vision-based counting eliminates the shortfalls and overages that manual tally and weight-based estimates let through.
Accurate Part Counts, Every Batch
Counting that never loses track
Whether parts are dropping through a chute, moving along a belt, or already sitting in a tray or stack, vision-based counting catches the shortfalls and overages that manual tally and weight-based estimates miss — at full accuracy.
Why counting goes wrong
Manual counts and weight estimates both drift
A missed capsule in a bottle, a short stack of rings before assembly, an SKU count that doesn't match the dispatch manifest — these are quiet, expensive errors. Manual counting is slow and fatigues; weight-based estimation breaks down the moment part weight varies. Vision counts the actual units, every time, and logs the result.
Three ways parts are counted
Pick the setup that matches your line
Free-Fall Counting
Parts dropping through a chute, spout, or funnel — capsules, tablets, fasteners, kernels — counted as they fall, at high speed.
ExploreConveyor-Based Counting
Parts moving continuously along a belt or line — bottles, pipes, packaged goods — counted in motion, encoder-synced to line speed.
ExploreStatic Tray / Stack Counting
Parts already arranged and stationary — a stack of rings, a tray of vials, a pallet face — counted in a single capture.
ExploreCapabilities
What this solution delivers
Full line speed
Counts every unit inline — no sampling, no slowing the line down to check a batch.
Auto reject on mismatch
Flags or diverts any pack, stack, or batch that doesn’t match the required count before it moves downstream.
Typical applications
Where it's used
Capsules & tablets
Fasteners & small hardware
Rings & stacked components
Vials & ampoules in trays
Pipes, bars & packaged goods
Pallet / SKU face counts
Proven in production
Case studies & app notes
Case studies
AUTOMOTIVE INDUSTRIESCASE STUDYCounting Piston Rings with 100% Accuracy Using Machine Vision
Jun 17, 2021READ →
PHARMACASE STUDYVial Counting Using Image Processing to Improve Inspection Accuracy and Reduce Downtime
May 5, 2022READ →
LOGISTICS AND PACKINGCASE STUDYCount the SKUs in a Pallet
Aug 12, 2021READ →
App notes
FMCGAPP NOTEMulti-Lane Biscuit Counting with One Camera
Single camera above the post-oven conveyor simultaneously counts biscuits, detects broken pieces, and alerts on lane imbalance across 2–3 lanes.
READ →
FMCGAPP NOTEBottle Cap Counting with In-Line Defect Detection
Single AI camera simultaneously counts bottles and detects missing, skewed, wrong-colour, and cracked caps in one inference pass — with a FSSAI-compliant timestamped image archive.
READ →
INDUSTRYAPP NOTEAutomatic Part Counting Using Machine Vision and Deep Learning
Instance segmentation-based counting for sub-5 mm micro-pins to 200 mm cable assemblies — 99.5%+ accuracy in under 2 seconds per batch, with ERP integration.
READ →
FMCGAPP NOTEDense-Pack Tray Counting for FMCG Processing Line
Vision-based count verification and SKU traceability for cigarette buds — replacing assumed tray fill counts with measured, logged, and auditable results at every tray.
READ →
Proven in production
Case studies & app notes
Case studies
AUTOMOTIVE INDUSTRIESCASE STUDYCounting Piston Rings with 100% Accuracy Using Machine Vision
Jun 17, 2021READ →
PHARMACASE STUDYVial Counting Using Image Processing to Improve Inspection Accuracy and Reduce Downtime
May 5, 2022READ →
LOGISTICS AND PACKINGCASE STUDYCount the SKUs in a Pallet
Aug 12, 2021READ →
App notes
FMCGAPP NOTEMulti-Lane Biscuit Counting with One Camera
Single camera above the post-oven conveyor simultaneously counts biscuits, detects broken pieces, and alerts on lane imbalance across 2–3 lanes.
READ →
FMCGAPP NOTEBottle Cap Counting with In-Line Defect Detection
Single AI camera simultaneously counts bottles and detects missing, skewed, wrong-colour, and cracked caps in one inference pass — with a FSSAI-compliant timestamped image archive.
READ →
INDUSTRYAPP NOTEAutomatic Part Counting Using Machine Vision and Deep Learning
Instance segmentation-based counting for sub-5 mm micro-pins to 200 mm cable assemblies — 99.5%+ accuracy in under 2 seconds per batch, with ERP integration.
READ →
FMCGAPP NOTEDense-Pack Tray Counting for FMCG Processing Line
Vision-based count verification and SKU traceability for cigarette buds — replacing assumed tray fill counts with measured, logged, and auditable results at every tray.
READ →
Not sure which counting setup fits your line?
Tell us how your parts move — free-fall, belt, or something else — and we'll scope the right approach.