SICK has launched a suite of Deep Learning apps and services to simplify machine vision quality inspection for challenging components, assemblies, surfaces or food produce, especially those that have previously defied automation and remained distinguishable only by human inspection.
SICK Deep Learning radically reduces set-up time and cost by enabling Artificial Intelligence image classification to run directly onboard SICK smart devices. With Deep Learning, programmable SICK devices take decisions automatically using specially-optimised neural networks and run accurate and reliable inspections that would have previously been extremely challenging or simply impossible to achieve in high-speed automated processes across many different industries.
Developed with user-simplicity at their core, SICK’s Deep Learning products cater for a wide range of needs and skill levels. The Deep Learning Starter App is designed for easy-set up by entry-level users, while the ready-to-use Intelligent Inspection Sensor App provides quick and easy integration with a large set of configurable machine vision tools. More experienced programmers and integrators can also create and customise their own Deep Learning sensor apps using the SICK AppSpace software platform.
Released as part of the initial launch, SICK Deep Learning is available using the Inspector P 621 2D vision sensor, and the SIM 1012 programmable Sensor Integration Machine generally running with SICK’s Picocam or Midicam streaming cameras. The longer-term roll-out will see SICK Deep Learning enabled across both SICK smart 2D and 3D vision sensors, and SICK data processing gateways.
With SICK Deep Learning, the image inference is carried out directly on the device in a short and predictable decision time, without the need for an additional PC, and results are output to the control as sensor values. Because system training is done in the Cloud, there is also no need for separate training hardware or software, saving on implementation time and cost.
