DEEP LEARNING
Dealing with data
Matthew Dale explores the software to simplify the management and labelling of deep learning data
D
eep learning has made waves in machine vision in recent years, both as a technology capable of
improving existing vision solutions, while also providing a solution to difficult vision challenges. Te technology largely simplifies the
creation of image processing algorithms. While in traditional machine vision a human is required to specify and evaluate specific image features to define the rules of an algorithm, with deep learning this can largely be automated. By simply showing neural networks either as whole images or regions of images from a labelled dataset, they can learn which features are relevant for classification purposes and automatically define rules for image processing algorithms. Tese algorithms can then be deployed on smart cameras and used to identify such features on images. Despite the promise deep learning has
shown, however, it should be noted that the tool will not be a silver bullet for all vision challenges, rather another arrow in the vision integrator’s quiver. Tis is because while some applications
benefit greatly from deep learning – such as the classification of amorphous objects such as faces, food products, bacteria colonies or parcels – for many industrial applications, particularly those where conditions can be managed and objects are already known, traditional vision methods are often preferred. In some cases, cleverly combining deep learning with traditional methods may yield the best results.
Dozens of datasets While having the potential to solve a great number of vision challenges, deep learning also introduces its own difficulties in that it creates a need to manage and label exceptionally large amounts of training data, which has been the cause of many
MVTec’s deep learning tool provides means to label data simply and efficiently 10 IMAGING AND MACHINE VISION EUROPE OCTOBER/NOVEMBER 2020 @imveurope |
www.imveurope.com
MVTec
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