In industrial applications including automotive and aviation, small manufacturing defects can lead to expensive or even catastrophic failures.
To prevent this, manufacturers rely on human inspection or costly and complex hardware optical systems.
However recent advances in camera processing and software development create the potential for lower cost, more flexible inspection systems.
This post will describe a method developed by OFH to identity scratches and surface defects as small as 10 microns. The system uses open source computer vision software, a low cost processor and inexpensive camera and illumination system.
The photo below shows the experimental setup including a custom back-light for dark field illumination and a test object. Two types of back-light: red coherent light and white light were tested. The object is a polished stainless steel with manually created scratches of different thicknesses (from 10 to 45 um).
For the detection a USB camera, laptop running custom code based on OpenCV + Python are used. The software methods used include blob detection, edge detection with find contours , adaptive color filtering, mathematical morphology and image histogram analysis. Depending on processor power more 50 frames per second can be analysed.
The results of this solution you can see on the images below.