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

TL;DR

This paper introduces a genetic algorithm-based framework to optimize parameters for stereo matching algorithms, significantly improving disparity map accuracy for UAV-based forestry imaging while maintaining efficiency.

Contribution

A novel GA-based parameter optimization method for stereo matching algorithms that automates tuning and enhances accuracy in UAV forestry applications.

Findings

Reduced Mean Squared Error by 42.86%

Increased Peak Signal-to-Noise Ratio by 8.47%

Improved Structural Similarity by 28.52%

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.