Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images

• URL: http://arxiv.org/abs/2512.05410v1
• Date: Fri, 05 Dec 2025 04:00:18 GMT
• Title: Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images
• Authors: Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green,
• Abstract summary: We propose a Genetic Algorithm (GA) based parameter optimization framework for UAV applications.<n>Our contributions include: (1) a novel GA-based parameter optimization framework that eliminates manual tuning; (2) a comprehensive evaluation methodology using multiple image quality metrics; and (3) a practical solution for resource-constrained UAV systems.<n> Experimental results demonstrate that our GA-optimized approach reduces Mean Squared Error by 42.86% while increasing Peak Signal-to-Noise Ratio and Structural Similarity by 8.47% and 28.52%, respectively, compared with baseline configurations.
• Score: 5.266753902938501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional stereo matching algorithms like Semi-Global Block Matching (SGBM) with Weighted Least Squares (WLS) filtering offer speed advantages over neural networks for UAV applications, generating disparity maps in approximately 0.5 seconds per frame. However, these algorithms require meticulous parameter tuning. We propose a Genetic Algorithm (GA) based parameter optimization framework that systematically searches for optimal parameter configurations for SGBM and WLS, enabling UAVs to measure distances to tree branches with enhanced precision while maintaining processing efficiency. Our contributions include: (1) a novel GA-based parameter optimization framework that eliminates manual tuning; (2) a comprehensive evaluation methodology using multiple image quality metrics; and (3) a practical solution for resource-constrained UAV systems. Experimental results demonstrate that our GA-optimized approach reduces Mean Squared Error by 42.86% while increasing Peak Signal-to-Noise Ratio and Structural Similarity by 8.47% and 28.52%, respectively, compared with baseline configurations. Furthermore, our approach demonstrates superior generalization performance across varied imaging conditions, which is critcal for real-world forestry applications.

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