Defects in the low contrast image on the left are hard to detect compared to the high resolution image on the right.
Poor quality images make training difficult for both the software and human graders, causing issues with classification and repeatability. To minimize false negatives and positives, try using high contrast images with 5 to 10 pixels describing the smallest defect. For example, when inspecting scratches on a smartphone, the machine vision will zoom in to focus on the image at the 5 micron resolution level. Having a high quality image helps human graders to validate the image, and the software to identify the difference between a scratch defect and acceptable machining marks.
When the deep learning vision system is ready for mass production inspection, consider using a two-tiered inspection approach. In tier 1, use automated inspection with deep learning machine vision on all parts. Then in tier 2, do manual confirmation of all borderline defective part results. This provides confidence and redundancy, as well as supplying data for incremental training improvement of the deep learning system.
Whether it’s used to locate, read, inspect or classify features of interest, deep learning-based image analysis is a fast and flexible way to improve part quality.
Learn how our custom, high-speed inspection systems identify and classify micron-level defects and surface flaws.