Digitale Unterstützung der Sichtprüfung mittels Deep Learning

Abstract

In accordance with IEC 60079-0, manufacturers of explosion-protected equipment are obligated to perform each routine test that is required in the 60079 series of standards. In addition, all necessary verification measures and inspections must be performed in order to ensure that the equipment corresponds to the documentation. [1] Overall, comprehensive quality assurance measures are required in order to ensure that explosion-protected products are safe. However, these are typically costly in terms of time, finances and staff. Visual inspections, in particular, are currently performed by members of staff to a large extent. Unfortunately, their ability to identify errors varies from person to person and depending on their alertness. The use of deep learning models offers huge potential for reducing the error rate in visual inspections. Smart object recognition enables product errors to be identified with consistent recognition accuracy. When it comes to traceability, too, the use of this technology offers many new opportunities. It enables data (image including estimate of object recognition algorithm) to be recorded during each visual inspection and assigned to the corresponding project.