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Scratch Inspector –  detection of surface defects with Machine Learning

EVT Eye Vision Technology

EVT Eye Vision Technology (Karlsruhe, Germany) has launched a machine learning based EVT Scratch Inspector which, is said, makes the detection of defects on surfaces “very easy.”

Based on a Deep Learning method, the Scratch Inspector detects any disturbance on the surface, similar to the human eye. Thanks to the Deep Learning base, the setup of the software is also straightforward.

The company also said the illumination has become a lot easier to handle. Whereas it is necessary in present common methods that the component part remains still for a moment during several images are captured, this method is based on scene illumination.

Therefore, the EVT ML Scratch Inspector only needs one image to detect the defect. This means that the component part does not have to be kept still and no elaborate illumination is necessary. A simple diffuse light is sufficient to detect scratches on the surface.

As standard the Scratch Inspector is available in three different illumination variations. Depending on the requirements, the illuminations can be easily adapted to the different sizes of the component parts.

The EVT Scratch Inspector is ready-to-use. The smart system has everything to integrate it directly into the plant. It has 8 in- and 8 outputs in 24 V technology to directly control a plant or to communicate with the PLC and the system is also equipped with a Profinet option. Therefore it is sufficient to connect the EVT Scratch Inspector to the PLC.

The system can be run as stand-alone system as well as headless sensor, which can be remotely programmed as OIP (Optical Inspection Processor) and integrated into the plant.

A completely ready-to-use alternative is the EyeMulti-Inspector. This system is a compact setup, where everything is integrated: camera hardware, processor and illumination. It can be positioned directly over the inspection location and all adjustments for camera, lens and illumination are included in the system.

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