Comparison · Inspection technology
AI or deterministic vision: which should you use for egg grading?
Deterministic vision uses classical operations with fixed rules and thresholds, such as colour, segmentation and morphology. Neural networks learn what defects look like from labelled examples. Deterministic vision gives the same result for the same image and explains each decision; a neural network can recognise variations nobody wrote down as a rule, but it needs labelled data, retraining and more effort to explain what it decided.
Updated · 4 min read
What are deterministic vision and neural networks?
Deterministic vision is classical computer vision used with explicit rules. It separates the egg from the conveyor by colour, splits touching eggs with watershed and measures dirt and cracks with filters and fixed thresholds. Each step can be read, tested and explained.
A neural network is a model trained on labelled examples. Instead of rules written by people, it adjusts many internal parameters until it fits the training examples.
Both approaches solve the same problem in different ways. The difference is how the criterion is defined: by rules the team can read, or by patterns learned from examples.
Which one is more reproducible and easier to audit?
Deterministic vision is more reproducible. With the same image and the same configuration the result is always identical, and an audit can repeat the decision using the measurements in the record.
With a neural network, the result for an image can change when the model is updated or retrained. Explaining why it chose a class is also harder, because the decision is spread across many weights rather than a readable rule.
Vigiovo uses the deterministic approach for this reason: each egg comes out with a class, scores, a readable reason and raw measurements, so the decision can be checked afterwards.
Deterministic vision or neural networks: how do they compare?
The table compares both methods on the criteria that matter most on a grading line. The deterministic column describes Vigiovo.
| Criterion | Deterministic vision (Vigiovo) | Neural networks |
|---|---|---|
| Result for the same image | Always the same | Can change with a new version or retraining |
| Explaining a decision | Readable rules and measurements | Hard to explain decision by decision |
| Data needed | Labelled images from your own line, for calibration | Many labelled examples that cover the defects |
| How it is adjusted | Thresholds and rules, at calibration | Retraining with new examples |
| Defects recognised | Only those its rules describe | Variations covered by the training, including ones nobody wrote down |
| Hardware | Ordinary CPU, about 90 frames per second on one core | Often relies on a GPU to keep up the speed |
| Audit trail | Record per egg, with reason and measurements | Record of the result; the reason needs extra explanation tools |
What is the limit of deterministic vision?
Deterministic vision only recognises what its rules describe. If a type of dirt or a crack pattern appears that the thresholds don't cover, the system can miss the defect or grade the egg wrongly until the rule is reviewed.
It can't see inside the egg either. The air cell, blood spots in the yolk and hairline cracks are out of reach of a surface camera, whatever the method. Those criteria need candling.
The cost is real: the rules have to be written and tuned by people who know the line. In return, the team knows exactly why an egg was rejected and can correct the rule when a new case shows up.
How do you choose between the two approaches?
Before deciding, work through these questions with the quality team:
- List the defects that must be separated and check whether each one can be described by rules visible in the image.
- Decide how much auditing and per-egg explanation your operation needs, for example for traceability.
- Assess how much your conveyor varies in light, shell colour and dirt, since variations no rule anticipates are the weak point of the deterministic approach.
- Test both options on labelled eggs from your own line before deciding, comparing the errors each one makes.
With deterministic vision, calibration is the most important step and lighting is the first thing to get right. See the calibrating with labelled eggs guide and lighting for egg inspection.
Frequently asked questions
Is deterministic vision the same as classical computer vision?
- In practice, yes. It uses operations such as colour segmentation, distance transform, watershed and morphology, applied with fixed rules and thresholds. No neural network is trained, and the same image always produces the same result.
Is deterministic vision more accurate than a neural network?
- There is no general answer. Deterministic vision is more predictable and easier to audit, but it only recognises the defects its rules describe. A well trained neural network can recognise variations nobody wrote as a rule, at the cost of labelled data, retraining and harder explanations.
Do I need labelled eggs to use deterministic vision?
- Yes, a set from your own conveyor. The dirt, crack and colour thresholds are tuned until the results match the labels your team made. That is calibration, not training a neural network.
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How Vigiovo helps
Vigiovo grades eggs on the conveyor as clean, dirty, cracked or unverified, in real time, with deterministic computer vision: every decision comes with the measurements behind it, on an ordinary CPU.