用遗传算法自动优化立体匹配参数，提升无人机测树枝距离精度。
Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images
- 用遗传算法自动搜索SGBM和WLS的最佳参数组合
- 误差降低42.86%，图像质量指标显著提升
- 适合资源受限的无人机林业应用，泛化能力强
传统立体匹配算法如半全局块匹配（SGBM）结合加权最小二乘（WLS）滤波器在无人机应用中具有速度优势，每帧生成视差图约需0.5秒。但这些算法需精细调参。本文提出一种基于遗传算法（GA）的参数优化框架，系统搜索SGBM与WLS的最优参数配置，使无人机能更精确地测量树枝距离，同时保持处理效率。贡献包括：(1) 无需人工调参的新型GA参数优化框架；(2) 使用多种图像质量指标的综合评估方法；(3) 面向资源受限无人机系统的实用解决方案。实验表明，相比基线配置，本方法使均方误差降低42.86%，峰值信噪比和结构相似性分别提升8.47%和28.52%。此外，该方法在不同成像条件下表现出更强的泛化能力，对真实林业应用至关重要。
原文摘要 · Abstract (English)
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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