a deep learning classification tool for the Matrox Imaging Library (MIL) 10 soſtware development kit. Te tool is able to run on a mainstream CPU – an Intel processor, for example – in millisecond timeframes. MVTec Halcon 18.05 also gives an optimised CNN inference for Intel-compatible x86 CPUs, offering runtimes of approximately 2ms. Czardybon noted that one advantage
– depending on your point of view – of training a neural network is that ‘it forces the customer to make up their mind about what is a defect and what is not’. He said during this presentation at the EMVA conference that one of the problems with applying deep learning is labelling images inconsistently. He advised to start by clearly defining test cases, and that any ambiguity must be resolved with the customer up front. Te soſtware doesn’t need programming – it’s one of the big benefits of deep learning – but to get the best results ‘the user must be aware that there is a training process that needs to be done correctly’, Czardybon commented. Czardybon also spoke about an application
where deep learning was used to map roads on satellite images. Here, the CNN wasn’t 100 per cent accurate, mainly because of tree cover confusing the neural network. He said that people might expect deep learning to work perfectly in this situation, and that this should be a lesson for industrial users: not to expect the impossible.
New kids on the block One of companies exhibiting for the first time at Vision in Stuttgart this year will be South Korean firm Sualab. Te company offers dedicated deep learning soſtware, which can be applied to machine vision applications – Hanjun Kim, marketing manager at Sualab, noted the Suakit has been used by Samsung, LG, Panasonic, Nikon, Hitachi, Hanwha, and SK. ‘Some of these customers are already applying our solution to the mass production line, which demonstrates our level of technology,’ he noted. Suakit has three core functions that include
segmentation, classification and detection. Te segmentation function is for detecting defects; classification categorises defects into types, while the detection function can detect each target object in an image by class. Suakit uses
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a pre-trained network and Kim recommends at least 100 images of each defect type for training. Te Vision show will also have deep learning
technology from Cognex on display, in the form of its VisionPro Vidi soſtware. Te tool can: locate and identify deformed features; separate anomalies and identify defects; classify texture and material; and perform demanding OCR applications. However, deep learning still poses some
challenges. Boriero at Matrox Imaging commented: ‘While the use of neural networks stands to accelerate and democratise the development of industrial imaging solutions and tackle inspection scenarios too ambiguous to resolve using traditional methods, it does pose significant challenges to users and suppliers of the technology. A balanced image dataset is needed to effectively train and validate a neural network for a specific categorisation job. Obtaining images of acceptable goods is much easier than of defective goods – especially when the latter comes in different occurrences that need to be tracked individually.
‘Users will need to
[Neural networks] will enhance our industry in such a way that engineers who solve machine vision problems now might get to a solution quicker
embrace the process of acquiring and labelling images, not only for initial development, but also for future adaptation,’ he continued. ‘Providers on their end will need to better deal with unbalanced image datasets and simplify the training process for the user by, for example, cutting down on
the trial-and-error approach to establishing the parameters for a successful neural network.’ Hiltner at MVTec concluded: ‘Deep
learning will not be a game-changer. It will not revolutionise our industry, but it will enhance it in such a way that engineers who solve machine vision problems now might get to a solution quicker. Tey can use CNNs to quickly get an idea whether deep learning would solve their problem. Before that, engineers had to spend a lot of effort programming, testing, and optimising algorithms and inspection systems. Now, this is straightforward; just use some labelled images and see whether deep learning is a good enough approach or not.’ He added: ‘You can very quickly decide
whether you are able to go on with a project using deep learning.’ O
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Visit us at the Vision in Stuttgart from 6th to 8th November 2018 Hall 1, booth 1H12
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