In just a few short years, deep learning software has improved to the point that it can classify images better than any traditional algorithm—and may soon be able to always outperform human inspectors
For many years, pet food manufacturers have used machine vision software to verify the presence of unique characters, codes, colors and graphic shapes on packaging for dog and cat food. Today, however, these companies can complement this process by also verifying the presence of a dog or cat image on the packaging using deep learning vision software.
Unlike traditional image-processing software, which relies on task-specific algorithms, deep learning software uses a multilayer network of neural self-learning algorithms to recognize good and bad images based on those that have been tagged as such by human inspectors. These data sets, which typically contain at least 100 images per defect type, are fed through the network to create a model that classifies objects within each input image and ensures a high level of predictability.