DEEP LEARNING


Neural classification


Greg Blackman on the deep learning soſtware tools now being deployed in industrial imaging


A


rtificial intelligence (AI) is beginning to infiltrate the machine vision world, just as it is in everyday life. Te


established industrial vision soſtware firms, such as MVTec Soſtware, Adaptive Vision and Matrox Imaging, are building neural network capabilities into their products, while firms specialising in deep learning and targeting industrial imaging are also springing up.


Deep learning technology is expected to be


present in abundance at the upcoming Vision trade fair in Stuttgart, from 6 to 8 November. Among the regular exhibitors with soſtware products – MVTec will run a two-hour seminar on deep learning at the show – there will be some new names on the exhibition floor: Sualab, a South Korean firm, which plans to unveil Suakit deep learning soſtware for machine vision; Deepsense, an AI company based in Warsaw, Poland; and Portuguese inspection company Neadvance.


Just a classifier Convolutional neural networks (CNNs) can outperform traditional algorithms – in some circumstances yielding impressive results – but


24 Imaging and Machine Vision Europe • August/September 2018


not in all, or even the majority, of situations. ‘A neural network is not the Holy Grail,’ stated Johannes Hiltner, product manager for Halcon at MVTec Soſtware. ‘It’s a great technology, but in the end, a neural network is just a classifier.’ Neural networks make decisions: is a part


good or bad, or class ‘A’, ‘B’ or ‘C’? Dr Jon Vickers from Stemmer Imaging, speaking at the UKIVA machine vision conference in Milton Keynes earlier in the year, noted that neural networks are good at tackling imaging problems where there is a lot of variation – inspecting organic material like fruit or wood, for example – and where a human would find it easier to show the machine the variation in images, rather than try and describe all those differences. A side effect of this, he noted, is


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