Intelligent machines need proper education
"Interestingly, algorithms - like people - can be lazy," he continues. "They will take any shortcut possible to make the pattern recognition process simpler. For example, if they learn that a certain pixel often has a particular value in the case of a 'bad' product, it will zero in on that one pixel and automatically label any image with that characteristic as 'bad' right off the bat. You have to keep testing to make sure that the model has been learning the right things."
As a result of the deep-learning model having been developed and trained, the client's computer vision system has both reduced the number of 'good' products being erroneously thrown out by 70% and increased the number of 'bad' components correctly identified by 30%.
"In order to strike the ideal balance between strict and effective that I mentioned before, we brought in an expert who is familiar with the products," Wouter explains. "The data provided by this expert helped the model to identify characteristics of the components that were okay to be 'bad' - such as superficial scratches versus. structural cracks. The previous solution was not subtle enough to identify these very slight differences."
Computer vision inspiration beyond quality control
Deep learning in computer vision has numerous applications in visual quality control inspection, but it can be used to overcome even more diverse challenges.
"For example, models can recognize very detailed anatomical structures in medical images, or identify exactly where and how many people or vehicles are entering and exiting specific areas in factories and facilities," Wouter elaborates. "In any system that uses visual data to discern characteristics and patterns, deep learning does it better, faster and more accurately than any traditional technology."
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