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A structure-oriented loss function for automated semantic segmentation of bridge point clouds
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-12 , DOI: 10.1111/mice.13422
Chao Lin, Shuhei Abe, Shitao Zheng, Xianfeng Li, Pang-jo Chun
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-12 , DOI: 10.1111/mice.13422
Chao Lin, Shuhei Abe, Shitao Zheng, Xianfeng Li, Pang-jo Chun
Focusing on learning-based semantic segmentation (SS) methods for bridge point cloud data (PCD), this study proposes a structure-oriented concept (SOC) with training focused on the spatial distribution patterns of bridge components, including both the horizontally absolute location of each component and its vertically relative position compared with other components. Then a structure-oriented loss (SOL) function, which embodies the core of SOC, is defined accordingly, and it is compared to five cutting-edge loss functions on a collected bridge PCD dataset. In contrast to the limitations of other loss functions, SOL significantly improves the overall evaluation metrics of overall accuracy (6.53%) and mean intersection over union (mean IoU: 8.67%). The IoU of the category “others” is improved by 8.44%, which is very important for automating the time-consuming denoising process. Furthermore, the demonstrated robustness of SOC and SOL reveal great potential to improve the performance of other SS models.
中文翻译:
用于桥梁点云自动语义分割的面向结构的损失函数
本研究专注于桥梁点云数据 (PCD) 的基于学习的语义分割 (SS) 方法,提出了一种面向结构的概念 (SOC),训练侧重于桥梁组件的空间分布模式,包括每个组件的水平绝对位置及其与其他组件相比的垂直相对位置。然后,相应地定义了体现 SOC 核心的结构导向损失 (SOL) 函数,并将其与收集的桥梁 PCD 数据集上的五个尖端损失函数进行了比较。与其他损失函数的局限性相比,SOL 显著提高了总体准确性 (6.53%) 和平均交集与并集 (平均 IoU: 8.67%) 的总体评估指标。“其他”类别的 IoU 提高了 8.44%,这对于自动化耗时的降噪过程非常重要。此外,SOC 和 SOL 的稳健性揭示了提高其他 SS 模型性能的巨大潜力。
更新日期:2025-01-12
中文翻译:
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用于桥梁点云自动语义分割的面向结构的损失函数
本研究专注于桥梁点云数据 (PCD) 的基于学习的语义分割 (SS) 方法,提出了一种面向结构的概念 (SOC),训练侧重于桥梁组件的空间分布模式,包括每个组件的水平绝对位置及其与其他组件相比的垂直相对位置。然后,相应地定义了体现 SOC 核心的结构导向损失 (SOL) 函数,并将其与收集的桥梁 PCD 数据集上的五个尖端损失函数进行了比较。与其他损失函数的局限性相比,SOL 显著提高了总体准确性 (6.53%) 和平均交集与并集 (平均 IoU: 8.67%) 的总体评估指标。“其他”类别的 IoU 提高了 8.44%,这对于自动化耗时的降噪过程非常重要。此外,SOC 和 SOL 的稳健性揭示了提高其他 SS 模型性能的巨大潜力。