Embedded system implementation of an evolutionary algorithm for circle detection on programmable devices

Rojas-Muñoz, Luis F.; Sánchez-Solano, Santiago; García-Capulín, Carlos H.; Rostro-González, Horacio

doi:10.1016/j.compeleceng.2022.107714

Cited by 3 publications

(2 citation statements)

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“…The design parameters presented in Table 1 were used in the development of the system. These parameters were selected based on the full software implementation of the detection strategy presented by Ayala et al in [16] and the preliminary implementation of a hybrid and optimized version of that strategy presented by Rojas et al in [26].…”

Section: System Descriptionmentioning

confidence: 99%

“…This paper presents a complete embedded system that uses a GA to detect circles in digital images based on the strategy published in [16] and whose HW implementation was detailed in [26]. The system, including support for I/O peripherals, has been implemented on an Ultra96-v2 development board, which is powered by a device of the Xilinx Zynq-Ultrascale+ MPSoC family that combines Programmable Logic (PL) with a Processing System (PS) based on a quad-core ARM Cortex-53 application processor.…”

Section: Introductionmentioning

confidence: 99%

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Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing

et al. 2022

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In recent years, the strategy of co-designing Hardware/Software (HW/SW) systems has been widely adopted to exploit the synergy between both approaches thanks to technological advances that have led to more powerful devices providing an increasingly better cost–benefit trade-off. This paper presents an HW/SW system for the detection of multiple circles in digital images based on a genetic algorithm. It is implemented on an Ultra96-v2 development board, which contains a Xilinx Zynq UltraScale+ MPSoC device and supports a Linux operating system that facilitates application development. The design is powered by developing an interactive computing environment by means of the Jupyter Notebook platform, in which different programming languages coexist. The specific advantages of each of these languages have been used to describe the hardware component that accelerates the evolutionary computation for circle detection (VHDL), to execute SW-HW interaction functions, as well as the pre- and post-processing of the images (ANSI-C) and to code, evaluate, and document the system execution process (Python). As a result, a computationally efficient application was obtained, with high accuracy in the detection of circles in synthetic and real images, and with a high degree of reconfigurability that provides the user with the necessary tools to incorporate it in a specific area of interest.

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Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%