Toward Intelligent Agents to Detect Work Pieces and Processes in Modular Construction: An Approach to Generate Synthetic Training Data
Toward Intelligent Agents to Detect Work Pieces and Processes in Modular Construction: An Approach to Generate Synthetic Training Data
复制标题
智能代理在模块化构造中检测工件和流程：一种生成综合训练数据的方法
DOI：
10.1061/9780784483961.084
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ergan, Semiha
中科院分区：
文献类型：
--
作者：
Park, Keundeok;Ergan, Semiha
模块化建筑已成为传统建筑工艺的替代方案，以减少对环境的影响和建筑废物，并解决高度密集的城市建筑工地的空间限制。此外，由于模块是在受控环境中预制的，因此与传统结构相比，模块化结构具有实现自动化和优化的优势。然而，由于建筑项目的独特性，与其他制造业相比，建筑业的自动化仍处于起步阶段。与此同时，最近，计算机视觉和深度学习等技术的进步为训练机器智能解决以前不可能解决的问题提供了机会。在这项研究中，我们提出了一种方法来自动生成高分辨率的合成训练数据的场景理解模块化建设的背景下。在试验台工厂环境中对该方法进行的评估表明，我们可以系统地捕获和标记RGB-D图像上的AEC组件，如墙壁和门，作为与模块化构建相关的监督学习应用程序的合成数据集。所提出的方法可以提供一种机制来馈送必要的但缺失的大规模数据集，以在模块化项目和相应的工件变化时在模块化建筑工厂中训练场景理解模型。
Modular construction has been an alternative to traditional construction processes to reduce environmental impact and construction waste as well as to deal with space constraints in highly dense urban construction sites. Furthermore, since modules are pre-fabricated in a controlled environment, modular construction has the advantage to achieve automation and optimization as compared to traditional construction. However, due to the one-of-a-type nature of construction projects, automation in construction is still in its infancy as compared to other manufacturing industries. Meanwhile, recently, advancements in technologies such as computer vision and deep learning provide opportunities to train machine intelligence to solve problems that were not possible before. In this study, we propose an approach to automatically generate high-resolution synthetic training data for scene understanding in the modular construction context. Evaluation of the approach in testbed factory settings shows that we can systematically capture and label AEC components such as walls and doors on RGB-D images as synthetic datasets for applications of supervised learning in relation to modular construction. The proposed method can provide a mechanism to feed the necessary but missing large-scale datasets to train scene understanding models in modular construction factories as modular projects and corresponding workpieces change.
影响因子：
6.6
作者：
Linda B. Smith
通讯作者：
Linda B. Smith