This short 'decision tree' will help you find out the answer.
Keeping in mind these two points, we design automated visual inspection systems that ensure high production quality by reducing both false positive and false negative test results and minimize efforts required through the automation of monotonous visual inspection, grading and sorting tasks.
Automated visual inspection software can analyse images to detect and classify the following features:
Depending on your specific needs, we will design and develop a solution that fits them best.
Machine vision has a number of advantages in comparison with human vision:
Besides that, the automated visual inspection systems have several pros that are intrinsic to the entire machine kind:
The AVI system consists of:
As a software development company, ScienceSoft focuses on the “soft” part of the AVI system: we create applications that analyze images captured by a digital camera starting with the preprocessing and up to the final decision step.
Based on a decision derived in a complex process described above, automated visual inspection software shows the results on a display and commands to take an action depending on the challenge posed. It can automatically discard defective items or mark them for further human inspection. It can also sort and grade objects according to predetermined parameters, such as shape, size and color (e.g. automated apple grading).
We deliver custom machine vision software tailored to the specific requirements of manufacturers working in the following industries:
Employing an AVI system for quality control in the production line brings a range of benefits. In particular, it helps:
Several challenges can affect the accuracy of automated visual inspection. However, it is possible to overcome them either by adjusting the initial conditions of acquiring raw digital data or by implementing special methods at the image analysis stage.
Object variability: For complex objects, significant variations exist in the appearance of both normal and defective items.
Solution: dataset augmentation.
The decision-making part of the AVI system is based on machine learning methods. In order to learn how to distinguish between normal and defective objects, it needs a large dataset of possible variations in their appearance. Having enough data to train this machine learning algorithm, we can ensure its ability to make accurate decisions.
Very small and non-distinctive details: Recognizing features and objects may be difficult if they have a very small size (such as PCA components) or melt into background (a grey caption on a grey background).
Solution: implementing advanced image preprocessing, segmentation and feature extraction techniques.
Machine vision software uses a number of techniques to distinguish indistinguishable. Depending on the task, our specialists will select the most appropriate techniques to enhance the image and extract valuable information.
Illumination variability: Changes in the illumination affect the resulting images creating new lines and disguising existing ones.
Solution: 1) ensuring uniform lighting conditions; 2) implementing advanced algorithms.
Although uniform illumination makes automated visual inspection much easier, it is possible to mitigate the effects of bad illumination to some extent using more sophisticated preprocessing and edge detection algorithms.
ScienceSoft has been delivering software consulting and solutions for 30 years already. Our team of 550 IT consultants, software architects, developers and testers is ready to solve any challenges that automated visual inspection can pose.
Our software consultants, in their turn, are eager to discuss your needs and requirements and suggest the most appropriate solution.
Libraries and frameworks:
We are always ready to consult you and propose a solution fitting your inspection requirements best. Feel free to contact us to receive more information and set up a fruitful partnership.