Deep learning software automate a vast assortment of production functions that are impractical for human
workers and rule-based algorithms.
Consider the inspector working on an automotive production line: doors, fenders, seats, windows, and hundreds more components can get scratched, dented, ripped or chipped along the way. Humans can catch some of those defects. Machine vision systems with high-tech cameras and complex algorithms can flag a few more well-
defined, predetermined flaws.
The trouble is that all the variables in a production setting can produce imperfections that are impossible to anticipate. That is where deep learning software comes to the rescue: It uses digital cameras and image recognition algorithms to learn to identify a broad spectrum of problems like rust, discoloration, and damage.
When developed properly, deep learning applications help manufacturers reduce errors and improve product quality. The software to build deep learning applications for manufacturing must have four core capabilities:
1. Feature Location and Assembly Verification
Finding flaws isn’t the only role for machine vision and deep learning software. It also can use training images and learning algorithms to locate specific components. This is essential for high-precision products like semiconductors, smartphones and pharmaceuticals. These applications also can scan the number of products in a location and tell a robot to keep adding more of the same products until a shelf or carton is full. They also can count all the components in a package to ensure that nothing has been left out.
2. Defect Detection and Segmentation
Identifying defects is perhaps the most sought-after capability for machine learning software in production environments. While machine vision systems can be programmed to flag one kind of flaw, identifying multiple flaws in this way is far too time-consuming.
Defect-detection tools start with a base of “good” images and pictures of common flaws like rust, dents, scratches and misalignments. Images of rare production outcomes can help the tool teach itself to improve its accuracy.
3. Object and Scene Classification
Classifying objects and scenes helps deep learning applications divide flaws into classes, which helps optimize the application’s ability to self-improve without human intervention. In general, images are labeled according to certain characteristics and then classified according to specific parameters. The best classification tools establish tolerances for natural deviations in shades, shapes or dimensions, and vary these tolerances according to the needs of each class.
4. Text and Character Reading
Deep learning applications connect fonts and typefaces with the lettering on parts in production. This makes it
much easier to read numbers or text through plastic covers and on uneven surfaces like clothing or gardening tools. Advanced character reading tools transcend the factory floor, finding a role in sophisticated distribution, logistics and commerce systems.
Cognex Deep Learning has these and many more powerful features designed precisely for factory and production environments, unlike other open source deep learning frameworks. It combines a comprehensive machine vision tool library with advanced deep learning tools inside a common development and deployment framework.