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Deep Learning and Hybrid Approach for Particle Detection in Defocusing Particle Tracking Velocimetry

Sax, Christian ORCID iD icon 1; Dreisbach, Maximilian ORCID iD icon 1; Leister, Robin ORCID iD icon 1; Kriegseis, Jochen ORCID iD icon 1
1 Institut für Strömungsmechanik (ISTM), Karlsruher Institut für Technologie (KIT)


The present work aims at the improvement of particle detection in defocusing particle tracking velocimetry (DPTV) by means of a novel hybrid approach. Two deep learning approaches, namely faster R-CNN and RetinaNet are compared to the performance of two benchmark conventional image processing algorithms for DPTV. For the development of a hybrid approach with improved performance, the different detection approaches are evaluated on synthetic and images from an actual DPTV experiment. First, the performance under the influence of noise, overlaps, seeding density and optical aberrations is discussed and consequently advantages of neural networks over conventional image processing algorithms for image processing in DPTV are derived. Furthermore, current limitations of the application of neural networks for DPTV are pointed out and their origin is elaborated. It shows that neural networks have a better detection capability but suffer from low positional accuracy when locating particles. Finally, a novel Hybrid Approach is proposed, which uses a neural network for particle detection and passes the prediction onto a conventional refinement algorithm for better position accuracy. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000158888
Veröffentlicht am 23.05.2023
DOI: 10.1088/1361-6501/acd4b4
Zitationen: 2
Web of Science
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Strömungsmechanik (ISTM)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 12.05.2023
Sprache Englisch
Identifikator ISSN: 0957-0233, 1361-6501
KITopen-ID: 1000158888
Erschienen in Measurement Science and Technology
Verlag Institute of Physics Publishing Ltd (IOP Publishing Ltd)
Band 34
Heft 9
Seiten Art.Nr.: 095909
Nachgewiesen in Web of Science
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