Computer Vision / AI
Visual Inspection & Tracking System
A camera-based pipeline that detects, counts and inspects items moving through a loading area, and turns each event into searchable data.
- Object detection
- Multi-object tracking
- Label OCR
- Edge inference
- Review interface
- Event alerts
Context
A logistics operation relying on manual counts and spot checks at loading bays, with existing cameras used only for occasional review.
The challenge
Counting and inspection happened on paper, so discrepancies were discovered days later.
Footage existed but was never searchable — finding one event meant scrubbing through hours of video.
Approach
- 01
Feasibility on real footage
Camera angles, lighting and image quality evaluated before any model work began.
- 02
Detection, tracking and OCR
Items detected and tracked across frames, with label text read and matched against expected shipments.
- 03
Edge-first deployment
Inference runs on-site; only events, crops and metadata are sent to the central system.
- 04
Humans review the uncertain cases
Low-confidence detections are queued for review, and corrections feed future retraining.
What we built
- Inference pipeline
- Event store
- Review queue
- Search by label or time
- Alerts
- Operations dashboard
- Python
- PyTorch
- OpenCV
- ONNX Runtime
- PostgreSQL
- Next.js
Outcome
- Counts recorded automatically per bay and per shift
- Every event searchable by time, bay or label text
- Uncertain detections routed to people instead of silently accepted
Next case study
Client Service Portal & Mobile App
Complex problem? Good.
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