ARTIFICIAL INTELLIGENCE
or yield, or areas where it is challenging to build a classical algorithm for a task. MVTec is offering training courses on
deep learning to give a better understanding of where it can and can’t be applied. Munkelt said there is an onus on suppliers to explain what neural networks can achieve and how much effort is required to develop a system. Hall also made the point that, in the machine vision market, a lot of the OEMs that are developing cameras are not necessarily the same companies that will be training and deploying the model. He said: ‘It’s a big question for a lot of machine vision camera suppliers: how do they enable their customer to integrate their own custom deep learning model onto these hardware platforms?’ Te development effort can be
considerable when working with neural networks. Vassilis Tsagaris, CEO of Irida Labs, noted it is easy to design an initial solution with 80 per cent success, but it’s difficult to move to a fully scalable solution. ‘You need an infrastructure not only for training and deploying, but also for taking care of the lifecycle of the product,’ he said. ‘You need to put the user in the loop [for real-world deployment]; you need to define the objectives, understand how you are going to work with data – more data
doesn’t necessarily mean a better model… understand what type of detection is important, and have a holistic approach that will deploy and feed the model throughout the product lifecycle.’ He added: ‘A first prototype is easy, but going into production requires more effort.’ Tanner estimated that around 70 to 80
per cent of all the work in building a system based on neural networks revolves around
‘70 to 80 per cent of work building a system based on neural networks revolves around data’
data – its collection, preparation, and making sure it is without bias. ‘You need to know where the data is from, you need to annotate the data, you have to generate a ground truth to train the network,’ she said. ‘It takes lots of effort to collect and maintain a good and valid dataset.’ Tanner added that the dataset has to
be balanced and cover all scenarios with no holes, with examples of good samples in all possible situations and also all possible defects. If there are two Gaussian distributions in the dataset, and in the
real world only one of these datasets is represented, then the neural network won’t be able to make proper decisions for the second distribution. A neural network is only able to learn on the data, not on something it hasn’t seen before. Tsagaris agreed that data handling is
‘important for faster time to market and successful implementations’. He said there are different ways to make the best use of data – like augmentation methods – but it still comes down to field data as ‘what’s going to drive the success in machine vision’. Irida Labs provides AI on embedded
platforms, and Tsagaris believes that in the future there will be a convergence between computer vision, AI and embedded vision. Munkelt added that in the coming years the ecosystem will be understood better, and there will be more knowledge around when to perform tasks in the cloud or on the edge, and where deep learning adds value. O
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Share your experience of developing a vision system using deep learning. Please get in touch for opportunities to write for us:
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