集中复制粘贴：面向野火语义分割的增强数据增强策略
Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
AI总结：
本文提出集中复制粘贴数据增强方法，通过识别火点、集中处理和粘贴提升野火分割性能，尤其在小样本数据下表现优异。
摘要：
收集和标注图像以训练分割模型通常是成本高昂的。在野火科学领域，这一挑战进一步加剧，因为可靠标注的公共数据集稀缺。本文提出集中复制粘贴数据增强（CCPDA）方法，旨在帮助训练深度学习多类分割模型，特别关注提升火类分割效果。CCPDA包含三个步骤：（i）在源图像中识别火簇，（ii）应用集中技术聚焦火区核心，（iii）将优化后的火簇粘贴到目标图像上。该方法在增加数据集多样性的同时保留火类本质特征。通过数值分析和基于加权和的多目标优化方法，与各种其他增强方法进行比较，证明了该增强技术的有效性。此方法有助于提升火类特定的分割性能指标，其操作意义显著高于其他类别（燃料、灰烬或背景）。数值性能评估验证了所提CCPDA方法在缓解小样本手动标注训练数据困难方面的有效性，并展示了其在考虑的应用场景中优于其他增强策略，特别是在提升火类分割性能方面。
英文摘要：
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire-class. CCPDA has three main steps: (i) identify fire clusters in the source image, (ii) apply a centralization technique to focus on the core of the fire area, and (iii) paste the refined fire clusters onto a target image. This method increases dataset diversity while preserving the essential characteristics of the fire class. The effectiveness of this augmentation technique is demonstrated via numerical analysis and comparison against various other augmentation methods using a weighted sum-based multi-objective optimization approach. This approach helps elevate segmentation performance metrics specific to the fire class, which carries significantly more operational significance than other classes (fuel, ash, or background). Numerical performance assessment validates the efficacy of the presented CCPDA method in alleviating the difficulties associated with small, manually labeled training datasets. It also illustrates that CCPDA outperforms other augmentation strategies in the application scenario considered, particularly in improving fire-class segmentation performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
