The papers show you models trained on clean datasets in controlled conditions. The logistics environment is not that.
We've been running Countercheck pilots in logistics facilities for about six months. Here's the gap between what the literature prepares you for and what you actually encounter.
Lighting
Warehouses have bad light. Not uniformly bad. Variably bad. Fluorescent overheads that flicker. Loading dock areas that get direct sunlight for three hours in the afternoon and none the rest of the day. Shadowed corners where products stack up. Motion-triggered lights that create sudden transitions.
A model trained on images with consistent lighting will fail on distribution shift from lighting variation. You can prepare for this partially with aggressive data augmentation during training: random brightness, contrast, shadow simulation. What you can't fully simulate is the specific combination of lighting artifacts that appears in a specific facility at a specific time of day.
The practical answer is to collect data from the deployment environment, not just from controlled captures. This sounds obvious. Most teams skip it because it requires physical presence at the deployment site during normal operations. It's worth the trip.
Damaged packaging
Products in logistics are not in pristine condition. They've been in transit. Boxes are dented, crushed, partially opened, taped with repair tape over original markings, stacked sideways. The product the model was trained to authenticate doesn't look like the product it encounters in production.
This is a domain-specific version of a general problem: test-set distribution differs from deployment distribution. The specific form it takes in logistics is packaging degradation. A model that achieves high accuracy on clean product images can fail significantly on the images it will actually see.
We address this by including damaged packaging explicitly in the training data. It requires collecting it, which means working with the logistics partners to capture images during normal operations rather than staging clean samples. The model performance on damaged packaging is lower than on clean packaging and that's fine because we're honest about it in the product.
Occlusion
Products are rarely perfectly centered and fully visible. They're partially behind other products, stacked at angles, partially out of frame. The detection model has to handle partial visibility reliably.
YOLO handles occlusion reasonably well on standard benchmarks. The logistics-specific version is harder because the occlusion patterns are systematic rather than random. Products on a conveyor belt are always partially occluded in the same ways based on how they're loaded. The model needs enough variation in the training data to generalize across the specific occlusion patterns of different facilities.
Throughput vs. accuracy tradeoffs in real deployments
In a pilot environment where you control the conditions, you can hit high accuracy numbers. In a full production deployment where the line is running at normal speed, the image capture quality degrades. Motion blur. Sub-optimal angles. The model has to make predictions on inputs that are worse than what it trained on.
We've tuned the confidence threshold deployment by deployment. A lower confidence threshold catches more counterfeits and produces more false positives. A higher threshold is more conservative. The right setting depends on the specific operation: a facility running high-value items sets the threshold differently than one running high-volume commodity goods.
The tuning requires running the system in production for long enough to see the actual distribution of confidence scores. You can't determine the right threshold from a pilot. You determine it from production data.
What good system design looks like
The model is one component. The system design around it matters as much. Camera placement, image capture triggers, hardware specs, retry logic for low-confidence predictions, human review workflows for borderline cases.
The teams that succeed with CV in logistics are the ones that think about the system, not just the model. The model is a solved problem for most logistics use cases. The deployment is where the real work is.
With gusto, Fatih.