{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:01:03Z","timestamp":1771916463029,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Convolutional neural network (CNN)-based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application for visual quality evaluation of X-ray scatter grids is described and evaluated. To detect the small defects on the 4K input images, a sliding window approach is chosen. A special characteristic of the selected approach is the aggregation of overlapping prediction results by applying a 2D scalar field. The final system is able to detect 90% of the relevant defects, taking a precision score of 25% into account. A practical examination of the effectiveness elaborates the potential of the approach, improving the detection results of the inspection process by over 13%.<\/jats:p>","DOI":"10.3390\/s22030811","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence-Based Assistance System for Visual Inspection of X-ray Scatter Grids"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-0716","authenticated-orcid":false,"given":"Andreas","family":"Selmaier","sequence":"first","affiliation":[{"name":"Institute for Factory Automation and Production Systems, Friedrich-Alexander University, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Kunz","sequence":"additional","affiliation":[{"name":"Institute for Factory Automation and Production Systems, Friedrich-Alexander University, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominik","family":"Kisskalt","sequence":"additional","affiliation":[{"name":"Institute for Factory Automation and Production Systems, Friedrich-Alexander University, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Benaziz","sequence":"additional","affiliation":[{"name":"Technology Center for Power and Vaccuum Components, Siemens Healthineers, 91052 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jens","family":"F\u00fcrst","sequence":"additional","affiliation":[{"name":"Technology Center for Power and Vaccuum Components, Siemens Healthineers, 91052 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Franke","sequence":"additional","affiliation":[{"name":"Institute for Factory Automation and Production Systems, Friedrich-Alexander University, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","unstructured":"Fuhr, T., Makarova, E., Silverman, S., and Telpis, V. 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