Fast and comprehensive FPGA based BLOB analysis with the Hybrid-BLOB concept
Simon Wezstein, Michael Stelzl, Michael Heizmann
Kapitel/Beitrag aus dem Buch: Längle T. & Heizmann M. 2022. Forum Bildverarbeitung 2022.
Part of
Kapitel/Beitrag aus dem Buch: Längle T. & Heizmann M. 2022. Forum Bildverarbeitung 2022.
In this contribution we show our approach for a feature rich and high speed BLOB analysis on FPGAs. For the Hybrid-BLOB concept we use a combination of a single-pass BLOB analysis and a double-pass labeling algorithm. We use Basler’s VisualApplets for the implementation of the concept on their microEnable 5 frame grabbers. We achieve the extraction of the gray value data of the BLOBs at factor 14 higher frame rates compared to the naive labeling of the complete image. This is achieved by limiting the maximum BLOB size to 128×128 px, which speeds up the double-pass labeling algorithm. Our concept is targeted at low latency and high throughput demanding applications where BLOBs are small, like sensor based sorting or surface inspection.
Wezstein, S et al. 2022. Fast and comprehensive FPGA based BLOB analysis with the Hybrid-BLOB concept. In: Längle T. & Heizmann M (eds.), Forum Bildverarbeitung 2022. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000150865-11
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Veröffentlicht am 25. November 2022