Like artificial intelligence, machine vision is impressive, but it's just a tool. Its effectiveness depends on how the end user applies it. With that in mind, here are five key steps to integrating a machine vision system into defect detection.
Define the defect
The first step in the machine vision inspection process is to identify what constitutes a defect. Artificial intelligence is not as flexible as humans, nor does it possess the ability to think in minute detail; therefore, specific guiding principles are needed. First, list all defects that need to be inspected, such as dents, scratches, or misaligned parts.
Only 53% of AI projects make it to the prototype stage, and a lack of vision or focus is often a key issue. Therefore, you should define defects as precisely as possible. Be sure to include all defect cases previously encountered by quality assurance personnel, as well as all possible illustrative scenarios.
Effective defect identification relies on understanding the appearance of conforming and non-conforming products. You must clearly define these two categories to provide a reference for subsequent training and data collection.
Determine the appropriate technology
Next, determine which technology is most relevant to your needs. Your definition of the defects from the first phase should help you more easily understand the type of machine vision system you require.
If you only need to check the liquid level in a clear or semi-transparent bottle, a simple light sensor will suffice. If you must inspect a car's rearview mirror for various surface defects or misalignments, you'll need a more robust system that includes a camera or multiple laser sensors.
Choosing the right technology also applies to software. Incorrect hyperparameter tuning methods can significantly impact your results, and using the wrong machine learning model will produce the same effect. You may need to collaborate with external AI experts to help you find the most suitable solution.
Pay attention to lighting
Lighting is another important consideration. All visual inspections rely on light, so you must provide a suitable environment to enhance the contrast of the object to be identified and minimize the influence of other elements.
The ideal lighting environment varies depending on your specific system. Determine the wavelengths your vision system analyzes, and then look for lighting equipment that highlights these wavelengths, as well as filters that eliminate noise from other wavelengths. In all cases, use an opaque barrier to block sunlight, as sunlight obscures all wavelengths and affects detection results.
The direction of the light source is just as important as the type and intensity of the light. Test and compare different angles—from backlighting to front lighting, to using multibeams—to find the angle that works best for your system.
Provide sufficient data
The next stage in the machine vision inspection process is building and training your machine learning model. The most important consideration in this step is the data you input into your system. Low-quality data costs organizations $12.9 billion annually, so providing sufficiently reliable information is crucial.
The key here is having a sufficient number of informative examples of defects and qualified products. Collecting samples from actual workflows will yield the best results, but if you don't have any records at hand, you can intentionally create defects as examples. Be as comprehensive as possible, providing multiple samples for each type of defect.
This data must be in the same format as what the machine vision system actually analyzes. If you are training a real-time video analytics model, input real-time video, not still images. Use the same data types, specific file types, organization formats, and themes as in the actual implementation.
Monitoring results
Finally, you must measure the success rate of your machine vision inspection process. Determine relevant KPIs to measure results based on your goals, and then measure your current system before implementing machine vision. These readings will provide a benchmark for future comparisons.
When using the new system, measure against the same KPIs. Compare these results to the initial baseline to see how the process has improved. If there is no improvement, inquire why; if there is improvement, investigate which aspects were most successful.
As errors emerge, you may need to continuously improve your machine vision system over time. These adjustments may stem from hardware or algorithm training issues, but in either case, ongoing review is necessary so you can adapt as quickly as possible.
Machine vision inspection process needs to be planned
When applied correctly, machine vision can significantly improve the speed and accuracy of defect detection. Following these steps is crucial to making the most of this technology.
Machine vision inspection processes can be complex, requiring extensive planning for effective implementation. However, companies that understand these requirements and approach machine vision with care can reap significant returns on their investment.
