An Advanced Embedded Architecture for Connected Component Analysis in Industrial Applications
Menbere Tekleyohannes1,a, Mohammadsadegh Sadri1,b, Christian Weis1,c, Norbert Wehn1,d, Martin Klein2,e and Michael Siegrist2,f
1University of Kaiserslautern, Germany.
atekley@eit.uni-kl.de
bsadri@eit.uni-kl.de
cweis@eit.uni-kl.de
dwehn@eit.uni-kl.de
2Wipotec Wiege- und Positioniersysteme GmbH, Kaiserslautern, Germany.
emartin.klein@wipotec.com
fmichael.siegrist@wipotec.com
ABSTRACT
In recent years, connected component analysis (CCA) has become one of the vital image/video processing algorithms due to its wide-range applicability in the field of computer vision. Numerous applications such as pattern recognition, object detection and image segmentation involve connected component analysis. In the context of camera-based inspection systems, CCA plays an important role for quality assurance. State-of-the-art hardware architectures offer high performance implementations of CCA using field programmable gate arrays (FPGAs). However, due to their high memory-demand, most of these implementations inhibit a large resource utilization. In this paper, we propose a hybrid software-hardware architecture of CCA for an industrial application using Xilinx Zynq-7000 All Programmable System on Chip (SoC). By offloading the most resource consuming part of the algorithm to the embedded CPU, we achieved high performance, while reducing the required resources on the FPGA. Our proposed architecture saves more than 30% of on-chip memory (Block RAMs) compared to state-of-the-art hardware architectures without affecting the throughput. Furthermore, due to the embedded CPU, our system provides a versatile and highly flexible feature extraction at run-time without the necessity to reconfigure the FPGA.
