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.
Abstract (translated)
传统的立体匹配算法，如半全局块匹配（Semi-Global Block Matching, SGBM）结合加权最小二乘滤波（Weighted Least Squares, WLS），在无人飞行器（UAV）应用中比神经网络具有速度优势，能够在大约0.5秒内生成每帧的视差图。然而，这些算法需要仔细调整参数。我们提出了一种基于遗传算法（Genetic Algorithm, GA）的参数优化框架，该框架系统地搜索SGBM和WLS的最佳参数配置，使UAV能够以更高的精度测量到树干的距离，同时保持处理效率。我们的贡献包括：(1) 一种新颖的GA基参数优化框架，消除了手动调优的需求；(2) 使用多个图像质量度量的全面评估方法；以及 (3) 针对资源受限UAV系统的实用解决方案。 实验结果表明，我们基于GA优化的方法与基准配置相比，在减少均方误差（Mean Squared Error）方面提高了42.86%，同时将峰值信噪比（Peak Signal-to-Noise Ratio）和结构相似性（Structural Similarity）分别提升了8.47% 和 28.52%。此外，我们的方法在不同的成像条件下表现出色的泛化性能，这对于现实世界的林业应用至关重要。
URL
https://arxiv.org/abs/2512.05410
