A Genetic Algorithm Approach for Parabolic Curve Detection Enhanced by FPGA-Based Hardware Acceleration

Detecting rotated parabolic shapes in digital images remains a significant challenge in computer vision, especially in embedded environments constrained by computational and memory resources. This study introduces a novel field-programmable gate array (FPGA)-based genetic algorithm (GA) architecture specifically tailored for rotated parabola detection, implemented as an intellectual property (IP) core on a PYNQ-Z1 system-on-chip (SoC) platform. The architecture encodes four parabola parameters into fixed-length chromosomes, assesses their geometric consistency with a 640 × 480 binary edge image using a hardware fitness function, and executes the entire evolutionary process in programmable logic. Image pre-processing is executed on an external CPU, using Canny edge detection for synthetic images and Holistically Nested Edge Detection (HED). For real images, post-processing and result visualization are conducted on the ARM processor using the PYNQ framework. Experimental results on synthetic images demonstrate mean accuracies of 98.47% and 95.23%, with detection success rates of up to 96%. For real images, since manually annotated ground truth is not available, results are presented as qualitative observations of convergence consistency across 100 independent runs. These findings demonstrate the feasibility of detecting rotated parabolas on resource-constrained embedded platforms and indicate promising applications in domains where parabolic patterns are prevalent, such as structural inspection, biomedical imaging, and perception modules for autonomous vehicles and driver-assistance systems.