AIO custs costs for visual inspection

Fujitsu Laboratories of Europe’s CEO Dr Adel Rouz explains the significance of the new technology breakthrough, “This is the latest in a long line of Fujitsu’s novel AI technologies, applying our advanced data analytics and machine learning expertise to address complex non-intrusive testing applications. Once provided with a high volume of low-cost and easily available defect-free samples, our new defect detector technology is able rapidly to work out what to look for automatically. This simplifies and accelerates the creation of machine learning solutions, as well as enabling the detection of previously unknown anomalies. Combined with our new AI-assisted GUI, LabelGear, we have engineered a powerful new visual inspection tool that can be applied to a wide variety of tasks, reducing costs, improving accuracy and accelerating the overall process.”

Potential applications include manufacturing, where cameras are placed at key points on a production line, continuously monitoring product quality and identifying any potential defects. In the steel industry for example, where a 2km long steel coil is produced every hour, some 70,000 images are used to capture the surface of a single coil and over 1 million during the course of a day. With Fujitsu’s automated unsupervised solution, 80-90% of the defects can be automatically identified and assigned appropriate labels, covering around 200 types of defects that need to be recognised. Other potential applications include infrastructure monitoring and healthcare, where the solution can be used to diagnose and screen for abnormalities, such as chest abnormalities (using X-ray scans). In the US alone, around 150 million such health checks are performed each year. Manually labelling the enormous volume of resultant images is prohibitively costly and time-consuming but becomes feasible with Fujitsu’s automated unsupervised visual inspection technologies.