DEEP LEARNING


that neural networks generally perform better than traditional algorithms with noisy images, or where there are occlusions. Neural networks can ‘exceed our


expectations and recognition rates of other classifiers, such as support vector machine (SVM)’, Hiltner observed. But for specific tasks, like locating objects very precisely in an image, making metric measurements, or reading barcodes, ‘traditional algorithms still outperform deep learning’. ‘Algorithms for locating an object based


on edge extraction, operating at sub-pixel precision, have been refined over the years, and a neural network would never reach this level of precision or speed,’ Hiltner continued. ‘You need dedicated algorithms to measure angles or distances between two edges. Tese algorithms outperform deep learning by orders of magnitude.’ In most cases, machine


vision tasks are solved by a combination of different algorithms, and deep learning is just part of the solution. Tere are few applications that can be solved in their entirety with only a neural network – one example is face detection. But industrial applications are more complex: first an image is acquired; then there’s pre- processing, then a measurement is made, and then the object might be inspected for quality. Te quality inspection part can be solved with deep learning, but post-processing or extracting the measurements of a defect are best done with traditional algorithms.


Sushi boxes can contain a lot of variation, and inspecting them is best solved with deep learning In one of its projects, MVTec worked with


Neural networks can exceed our expectations and exceed recognition rates of other classifiers


a logistics customer. Te task was to program autonomous vehicles in a warehouse to recognise whether high racks were empty – and could therefore have items dropped into them – or were already full. Tis is a simple classification problem with a lot of potential variety in the images: if a rack contains items there could be various possible shapes, colours and sizes. Te system had to be efficient but also cover the variation. Te customer solved the


application first without deep learning, using a 3D sensor to analyse whether the high rack was empty or not. Tis


approach had an error rate of 2.5 per cent. MVTec trained a CNN with image data from a standard 2D camera and achieved an error rate of 0.3 per cent. Te approach also reduced the cost of hardware – a 3D camera can cost around €3,000 while a 2D camera costs around €500 to €700, according to Hiltner. A second example is one of MVTec’s


Japanese customers, which builds machines for automatic pill packaging. Te machines produce thousands of pills per minute and defects can appear in various forms. ‘It’s hard to find a traditional algorithm that can differentiate between defects and required textures on the pills, like imprints or predetermined breaking points,’ said Hiltner. For a long time, the customer solved this


application with traditional algorithms; each time a new pill type was added to the line, an expensive machine vision engineer had to travel to the customer’s manufacturing site and spend several weeks programming new algorithms to include the new pill. MVTec realised it could speed this process


up by using labelled images showing different classes of defect to train a neural network. Hiltner explained: ‘We could significantly reduce the engineering time required for adding new defects or pill types, as well as reducing the cost of developing an algorithm to inspect specific pills.’ At the European Machine Vision


Association’s business conference in Dubrovnik, Croatia, in June, Michał


2D & 3D algorithms


HMI Designer


C++ and .NET libraries


Rapid development environment


Technical support and know-how


www.adaptive-vision.com


Deep Learning


Version 4.10


• new deep learning tools • improved performance


Adaptive Vision


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44