How to build a basic vision-based quality control system
Step 1: Define the inspection goal
Before you start coding, it’s important to be clear about the goal of your machine learning model. In most projects, the question is usually the same: Is the result obtained from my process correct, or does it have a flaw that prevents the process from functioning properly?
How to do it?
Step 1 Decide what the final output should be, for example PASS/FAIL or OK/DEFECTIVE.
Step 2 Focus only on the features that matter for the inspection task.
Step 3 Keep the first version simple and solve one problem well before adding more complexity.
Step 2: Collect and label example images
To build a good machine learning model, the most important thing is to have good data; a well-organized dataset that is useful for our ultimate goal—even if it’s smaller—will yield better results than a large, messy one.
How to do it?
Step 4 Capture images that represent both correct and defective cases.
Step 5 Make sure the labels are consistent and easy to understand.
Step 6 Split the data into training and test sets so the system can be evaluated properly.
Step 3: Preprocess the images
After capturing the images we’re going to use, it’s important to preprocess them so that our model can work with them more easily; this can be done in a variety of ways, such as cropping the relevant area, using ArUco markers, normalizing the view, or dividing the image into smaller regions.
How to do it?
Step 7 Remove unnecessary background information whenever possible.
Step 8 Keep the position and scale of the object as consistent as possible.
Step 9 Check intermediate outputs manually to make sure the preprocessing is actually helping.
Step 4: Train a simple classifier
Once the images have been prepared, you should choose the machine learning model that is best suited to the problem at hand. It is recommended to use a simpler, more reliable model rather than a complex one, especially if you have never worked with AI models before.
How to do it?
Step 10 Start with a standard model or a simple baseline.
Step 11 Train it using the labeled images and monitor its results on validation or test data.
Step 12 Save the best version of the model, not just the last one.
Step 5: Turn predictions into a practical decision
A model prediction is only useful if it leads to an action. In a quality control system, this usually means converting the output into a clear practical result that can be used by the rest of the process.
How to do it?
Step 13 Define a simple decision rule based on the model output.
Step 14 Make the final result easy to interpret, for example a clear PASS or FAIL.
Step 15 Test the full pipeline with new real images, not only with training data.